assumption for tractability, limiting practical impact. Solving network models
exactly is computationally hard. Accordingly, most studies on network models
use approximations, based on deterministic linear programming (Talluri and Van
Ryzin, 2004a), randomised linear programming (Talluri and Van Ryzin, 1999) or
decomposition into single-resource problems, as well as solutions using
simulation-based optimisation (Bertsimas and De Boer, 2005). Seat inventory
control usually assumes capacity to be fixed, an assumption relaxed by Büsing
et al. (2019) integrating capacity uncertainty in leg-based RM. Others
integrated inventory control and pricing (You, 1999).

Simplest deterministic pricing models are price-sensitive versions of the
well-known newsvendor problem (Gallego and Van Ryzin, 1994). This allows
mathematical derivations of optimal prices. Several studies, such as, Feng and
Gallego (1995), generalised this problem to include demand dynamics and/or
multiple products. Stochastic dynamic programming is a natural way to tackle
dynamic pricing. Dynamic models depict reality more accurately, but are harder
to solve (Gallego and Van Ryzin, 1994). Interestingly, solutions to
deterministic models are usually good approximations for their stochastic
counterparts, and are often used in practice. Traditional RM assumed
independent demand, ignoring product substitutability. With the seminal paper
of Talluri and Van Ryzin (2004a), the RM field has shifted toward including
customer choice behaviors within pricing and capacity decisions. §3.21
provides a detailed overview of RM concepts and trends beyond aviation.

#### Airline schedule planning (ASP)

ASP is the process of designing airline schedules maximising profits subject
to resource constraints. Taking demand, airport and aircraft characteristics,
and maintenance and personal requirements as inputs, ASP outputs selected
flight timetables, aircraft schedules and crew duty plans. Most ASP steps
typically occur before RM actions and thus constrain the set of decisions
available to RM systems. Key ASP steps include fleet planning, route planning,
frequency planning, timetable design, fleet assignment, aircraft routing and
crew scheduling. Fleet planning involves decisions regarding purchasing,
selling, and leasing of aircraft fleet, while route planning selects airport
pairs to operate nonstop flights. Early studies, e.g., Hane et al. (1995),
matched a predetermined set of flights with aircraft types, developing a fleet
assignment model (FAM). The FAM specifically focuses on fleet assignment,
which is one particular step within the overall ASP process. The basic FAM, a
mixed-integer linear program, minimised costs of operating aircraft and
passengers unserved, given passenger demand for individual flight legs. This
leg-based approach ignores that passengers often fly on multiple flights in
connecting itineraries.

Barnhart et al. (2002) overcame this limitation via an itinerary-based FAM to
explicitly model network effects. Some studies developed tractable solution
approaches. Barnhart et al. (2009) proposed a subnetwork-based decomposition
for capturing FAM’s revenue implications, an approach recently extended by Yan
et al. (2022a) to solve a FAM incorporating passenger choice. Others extended
FAM by incorporating incremental timetable design decisions, e.g., changes to
flight timings (Desaulniers et al., 1997) or selection of optional flights
(Lohatepanont and Barnhart, 2004). Wei et al. (2020) developed a clean slate
heuristic optimising entire timetables and fleet assignments under choice-
based demand. Frequency planning, which optimises the number of flights
operated during a day or part of a day, rather than deciding exact timetables,
has also received attention, with an emphasis on capturing affects of
competition from other airline and high-speed rail operators (e.g., Cadarso et
al., 2017).

The last two steps in schedule planning are conceptually similar. Aircraft
routing assigns individual aircraft to flights while ensuring that each
aircraft undergoes periodic maintenance, and crew scheduling assigns crew to
operate flights while satisfying a myriad of crew regulations. Early studies
individually optimised aircraft routing (Gopalan and Talluri, 1998) or crew
scheduling (Graves et al., 1993a). Lavoie et al. (1988) used column
generation, an effective solution approach for both problems, to crew
scheduling, while Cordeau et al. (2001) used Benders decomposition to jointly
solve both problems.

Good schedules not only minimise planned costs, but are also robust to
disruptions, to keep the actual costs low. Researchers in early 2000s
optimised robustness proxies, e.g., station purity, short cycles, crew
swapping opportunities, and crew schedule slack (Schaefer et al., 2005). Later
studies directly minimised total planned and unplanned costs of aircraft
routing (Lan et al., 2006) and crew scheduling (Yen and Birge, 2006)
separately, and also jointly (Dunbar et al., 2012). Recent studies have used
robust optimisation to solve the aircraft routing (Yan and Kung, 2018) and
crew scheduling (Antunes et al., 2019) problems.

#### Airline operations recovery (AOR)

AOR encompasses the actions undertaken to repair schedules, when disruptive
events such as inclement weather, equipment failures, etc., take place.
Rosenberger et al. (2003) developed a model and a solution heuristic for
repairing aircraft routing, whereas Lettovskỳ et al. (2000) tackled crew
recovery. For the integrated recovery problem, Petersen et al. (2012)
developed a decomposition strategy, while Maher (2016) used column-and-row-
generation. Recent recovery studies incorporated other key elements, including
flight planning (Marla et al., 2017) and passenger no-shows (Cadarso and Vaze,
2022).

#### Airport flight scheduling (AFS)

Beyond airline decision-making, OR is also used to improve decision-making of
central authorities and air traffic managers. Research over the past decade
demonstrated the potential for enhancing social welfare by constraining
schedules at busy airports via slot-control mechanisms (Swaroop et al., 2012).
Some studies balanced strategic cost of scheduling changes against tactical
cost of delays, for a single airport (Jacquillat and Odoni, 2015) or multiple
airports (Wang and Jacquillat, 2020). Zografos et al. (2012) used an integer
program for allocating slots to airlines under administrative controls.
Fairbrother et al. (2020) attempted to balance the often-conflicting goals of
efficiency, equity and the incorporation of airline preferences in optimising
slot-scheduling mechanism.

#### Air traffic flow management (ATFM)

The tactical side of airport and airspace capacity management has received
considerable OR attention since the 1990s. ATFM is a broad term used to define
key interventions, such as ground holding of airplanes, that ensure safe and
efficient flight operations by restricting flow of aircraft into congested
airspaces. Terrab and Odoni (1993) and Vranas et al. (1994) proposed the
single-airport and multi-airport ground holding problems, respectively. The
latter was extended to include enroute capacities by Bertsimas and Patterson
(1998). Bertsimas et al. (2011b) additionally incorporated flight rerouting
and solved larger-scale problems. Adoption of the collaborative decision-
making (CDM) paradigm in practice ushered in a new era of research. Advocating
increased agency to airlines, Vossen and Ball (2006) provided an integer
program for slot trading mechanism design under CDM. Recent studies (e.g.,
Starita et al., 2020) are increasingly focused on explicit handling of
uncertainty on both demand and capacity side within the ATFM optimisation
problems.

#### Modelling delay propagation

Tightly coupled aviation networks make disruption management particularly
challenging. Delays and disruptions in one part of the network propagate to
other parts, through aircraft, crew and passenger connections. Recent studies
quantified these propagation effects. First, Pyrgiotis et al. (2013) proposed
an analytical queuing and network decomposition model for aircraft-based delay
propagation. Barnhart et al. (2014) presented discrete choice models for
passenger itinerary estimation and a reaccommodation heuristic for passenger
delay calculations. Wei and Vaze (2018) solved inverse optimisation for
estimating crew itineraries and crew-based delay propagation. These studies
attempted bridging the gap between sparse and aggregate public datasets, and
the detailed and disaggregated data needs for aviation OR research.

#### Further reading

Readers interested in aviation OR are referred to the second edition of the
book by Belobaba et al. (2015). In particular, Chapters 4 and 5 focus on
pricing and RM, Chapters 8 and 10 on schedule optimisation, robustness and
recovery, and Chapter 14 on air traffic management and control. Looking ahead,
it is apparent that OR will keep finding natural applications within aviation,
especially given the exciting disruptive innovations within urban air
mobility. Rapidly growing fields of passenger air taxi operations and drone
operations for parcel deliveries are giving rise to new variants of well-known
OR problems, e.g., network design (Wang et al., 2022b), travelling salesperson
(Roberti and Ruthmair, 2021), vehicle routing (Dayarian et al., 2020), and
facility location (Chen et al., 2022).

###  3.31 Transportation: Network design848484This subsection was written by
Mike Hewitt.

In a transportation context, the term _Network Design_ (Magnanti and Wong,
1984) generally refers to planning the _supply_ side of a transportation
system so that it efficiently satisfies some estimate of _demand_ within the
quality standards of the customers using the system. The planning decisions
typically prescribe the movements of vehicles, or convoys (e.g., a railroad
train or tug and barges), between stations/terminals in the network to
transport people or goods. Network design is typically undertaken for
situations wherein what is transported, be it people or goods, is small
relative to vehicle capacity. Thus, one primary measure of efficiency is
vehicle utilisation, with high utilisation achieved through consolidation.
Quality is typically measured based on on-time delivery.

Network design is relevant to passenger transportation systems such as urban
public-transport (Mauttone et al., 2021) by bus (Ceder and Wilson, 1986) or
light rail (Farahani et al., 2013), as well as systems providing interurban
transport by train (Hooghiemstra et al., 1999) or airplane (Franke, 2017). It
is also relevant to a wide range of goods transportation markets, such as
parcel and small-package (Barnhart and Schneur, 1996) and less-than-truckload
freight (Powell and Sheffi, 1989). A network design case study for a postal
carrier can be found in Winkenbach et al. (2016). Transportation carriers
serving these markets may rely on one or more modes, including motor carrier
(Bakir et al., 2021), rail (Chouman and Crainic, 2021), ocean (Christiansen et
al., 2020), and inland waterway (Konings, 2003). The planning of vehicle and
goods movements by each mode and synchronisation of goods moving from one mode
to the next (e.g., intermodal) can be assisted by network design (Arnold et
al., 2004).

For different modes the scope of design decisions prescribed by network design
models may be broadened in different ways. For example, modes such as rail and
inland waterway involve multiple layers of consolidation. For rail (Zhu et
al., 2014), goods are consolidated into rail cars, which are then consolidated
into blocks that are transported by the same locomotive. For motor carriers,
vehicles can not yet move without a driver, whose movements and schedules are
restricted by governmental safety regulations and potentially labour
management practices that dictate the driver return periodically to a specific
physical location in the network (e.g., his/her domicile). Network design
models for motor carriers may build schedules for drivers that observe safety
regulations (Crainic et al., 2018) as well as determine how many drivers
should be associated with each physical location (Hewitt et al., 2019).

The network design problem is typically modelled as a Mixed Integer Program
(MIP) formulated on a directed graph (Crainic et al., 2021a). Nodes in such a
graph model physical locations, potentially at different points in time.
Directed edges between such nodes model transportation that begins in one
physical location and ends at another. Edges may encode a scheduling
dimension, such as when a vehicle departs from one location and arrives at
another, that depends in part on the travel time required for the physical
move (Erera et al., 2013). Associated with an edge is a function that maps the
amount of vehicle capacity made available on that edge to cost. Typically, it
is a step function with each step modelling an increase in capacity due to
dispatching an extra vehicle. Commodities model people or goods that are to be
transported; associated with each commodity is an origin node, a destination
node, and a size.

The classical network design problem seeks to find a path for each commodity
that begins at its origin node, ends at its destination node, and potentially
visits one or more intermediate nodes. The problem evaluates these paths with
respect to the total cost of capacity made available to support them and seeks
to minimise that total cost. Some network design models (Frangioni and
Gendron, 2021) instead minimise costs that are a function of the amount of
goods transported on an edge, as opposed to the capacity made available to
transport them. Network design is an optimisation problem that has received
significant attention both for its practical relevance and the computational
challenges (Johnson et al., 1978) associated with solving it.

Most MIP formulations of the network design problem involve commodity flow
variables that model the transportation of goods within the network and
another set of edge-based variables that model the transportation of vehicles.
Typically, commodity flow variables are continuous when a shipper’s goods can
be divided and routed on multiple paths or binary when they cannot. Commodity
flow variables are typically edge-based, but some models involve paths from
shipment origin to shipment destination. The use of a path formulation
typically necessitates column generation (Hewitt et al., 2019). However,
unlike the vehicle routing problem, extended, path-based formulations of the
network design problem do not provide stronger linear relaxations than
compact, arc-based formulations. Depending on the context and mode the vehicle
edge variables may either be binary or integer. Linking constraints are
included in the formulation to ensure sufficient vehicle capacity travels on
an edge to carry the commodities making that transportation move. Typically,
much larger cost coefficients are associated with vehicle edge variables than
commodity flow variables.

The majority of literature on network design focuses on deterministic models
wherein it is presumed all parameter values (costs, capacities, demands) are
known with certainty. However, given that network design models are often
solved as part of a tactical planning exercise, uncertainty has been studied
(Hewitt et al., 2021). Much of that work focuses on uncertainty in commodity
sizes and models such problems as two stage stochastic programs wherein
vehicle movements are planned in the first stage and commodities are routed in
the second stage given the vehicle movements prescribed in the first. There
has been limited work on robust optimisation models (Koster and Schmidt, 2021)
or those that view network design in a dynamic context (Al Hajj Hassan et al.,
2022).

Both exact (Crainic and Gendron, 2021) and heuristic (Crainic and Gendreau,
2021) solution methods for deterministic network design models have been
proposed. One challenge associated with solving MIP formulations of network
design problems is that the linking constraints often lead to fractional
vehicle edge variables. Thus, the linear programming relaxations of network
design MIPs often yield weak bounds on the objective function value of an
optimal solution to the MIP. As a result, much of the literature that focuses
on speeding up the solution of MIP formulations of the network design problem
focuses on strengthening formulations with valid inequalities (Nemhauser and
Wolsey, 1988). Such inequalities are typically either based on classical ideas
such as flow covers from integer programming (Gu et al., 1999) or leveraging
the network structure of the problem (Raack et al., 2011). Another approach
taken to solve network design problems is Benders decomposition (Benders,
2005; Costa, 2005), particularly when second stage variables are continuous
and the optimisation problem resulting from fixing the network design is a
linear program.

Another challenge associated with solving MIP formulations of the network
design problem is due to the size of the network on which the MIP is
formulated when that network encodes time. The classical approach to
representing time in network design is to formulate a MIP on a network wherein
multiple nodes represent the same physical location, albeit at different
points in time (Crainic et al., 2016). Similarly, multiple edges represent the
same physical transportation move, albeit at different departure and arrival
times. Such networks are typically referred to as time-expanded networks and
the overall solution procedure in contexts that require the modelling of time
is to construct such a network, formulate a MIP on that network, and then
solve that MIP. Boland et al. (2019) study the impact on solution quality of
modelling time at different granularities and observe that the finer the
representation the higher the quality of the resulting solution. However, such
an approach can be computationally challenging when long planning horizons
must be modelled or fine representations of time are used, as both cases lead
to networks and resulting MIPs that are very large. An alternate approach,
called Dynamic Discretisation Discovery (Boland et al., 2017; Hewitt, 2019)
proposed to instead generate time-expanded networks in a dynamic and iterative
manner.

Heuristic methods for deterministic network design models can be classified
into one of two categories. The first category focuses on metaheuristics
(Hussain et al., 2019) and neighbourhood structures. Early heuristics (Powell
and Sheffi, 1983) proposed for network design models searched neighbouring
solutions by reducing the capacity on one edge in the network and, if
necessary, increasing the capacity on another. However, more recent and
effective methods have proposed more complex neighbourhood structures such as
cycles or paths (Ghamlouche et al., 2003). The second category focuses on what
is generally called matheuristics (Maniezzo et al., 2021). In these
heuristics, a neighbourhood of a solution is searched by formulating and
solving the MIP of the network design problem, albeit with the values of
subsets of variables fixed to their values in the solution at hand (Hewitt et
al., 2010). This is repeatedly done and with different mechanisms used for
selecting subsets of variables to fix.

Similarly, both exact and heuristic solution methods have been proposed for
stochastic network design models that take the form of scenario-based two
stage stochastic programs. The vast majority of such stochastic programs
studied to date involve continuous commodity flow variables in the second
stage. As a result, the second stage subproblems are linear programs and the
overall stochastic program is amenable to Benders decomposition (Birge and
Louveaux, 2011). Thus, much of the methodological work on solving such
stochastic programs has focused on techniques for speeding up or rendering
more impactful different steps in the Benders scheme (Magnanti and Wong, 1981;
Crainic et al., 2021c). While Progressive Hedging (Rockafellar and Wets, 1991)
is an exact method for two stage stochastic programs with continuous variables
in both stages, it has been used as the basis of heuristic methods for
stochastic network design (Crainic et al., 2011, 2014).

Crainic et al. (2021b) contains deeper dives into the subjects touched on here
as well as discussions of those not discussed.

###  3.32 Transportation: Vehicle routing858585This subsection was written by
Claudia Archetti and Maria Battarra.

The Capacitated Vehicle Routing Problem (CVRP) was first proposed by Dantzig
and Ramser (1959), and named the Truck Dispatching Problem. The goal was that
of routing a fleet of identical gasoline delivery trucks from a central depot
to service stations (often referred as ‘customers’). Each truck had to return
to the central depot, after visiting an ordered subset of the customers. All
customers had to be visited once by a vehicle delivering all their gasoline
requirements in the one delivery. The objective was the minimisation of the
routing costs, as the sum of the travelling distances of every truck. The CVRP
classical definition is the same as that proposed by Dantzig and Ramser (1959)
more than 60 years ago. Introducing a capacitated fleet of vehicles makes the
CVRP for a much harder generalisation of the Travelling Salesman Problem
(Flood, 1956).

The CVRP definition has been enriched over the decades to take into account
all the delivery requirements of the customers and of the transportation
providers, as well as the characteristics of the available fleet of vehicles,
and the increasing availability of technology (i.e., GIS and real time
mapping, autonomous vehicles, shared mobility systems and so on). The research
literature has flourished with new variants, as well as more sophisticated and
flexible solution approaches. This chapter aims at providing pointers to key
milestones achieved in the last 60 years of the CVRP literature, identifying
the latest and most successful exact and metaheuristic algorithms, as well as
referencing the most famous online challenges and standard techniques for
benchmarking CVRP solution algorithms.

The CVRP ‘classical’ variants and solution approaches are well summarised in
Toth and Vigo (2002). This book provides key references and definitions for
critical application features, as for the CVRP with Time Windows, the CVRP
with Backhauls and the CVRP with Pickup and Delivery, the CVRP with
vehicle/site dependencies, the CVRP with inventory and the stochastic CVRP.
Golden et al. (2008) extends the definition of the classical variants to
routing problems with heterogenous fleets, periodic routing problems, split
routing problems, dynamic and online routing problems. Toth and Vigo (2014)
further widen the remit of application of routing algorithms to maritime
applications, disaster relief distribution problems, and considers up-to-date
objective functions different than minimising the distance travelled. More
recently, fleets of electric vehicles (Pelletier et al., 2016), problems over
time (Mor and Speranza, 2020), drones (Otto et al., 2018), cargo boats
(Christiansen et al., 2013) and warehouse pickers (Schiffer et al., 2022) have
been embedded in routing settings. The new dynamic environment inspired
research on stochastic (Gendreau et al., 2016), dynamic (Soeffker et al.,
2022) and time-dependent (Gendreau et al., 2015) routing problems.

An up-to-date survey on recent trends can be found in Vidal et al. (2020), in
which the CVRP extensions due to richer objective functions, the integration
with other optimisation problems, and application-oriented transportation
requirements are surveyed. Partyka and Hall (2014) discuss routing algorithms
from the practitioners’ perspective, and surveys which are the requirements of
a logistics company when they acquire a routing software.

Next the most successful CVRP solution algorithms are summarised, first
discussing exact methods. Formulations with a polynomial number of variables
and constraints were the first proposed mathematical models, as for the two-
commodity formulation by Laporte (1992) and Baldacci et al. (2004). They have
the advantage of being easy to use (as they just require encoding in the
syntax of the solver). The disadvantage of them however is their poor
performance due to high dimension of the formulations, and the weakness of the
continuous relaxation. Better results were obtained from formulations with an
exponential number of constraints, such as those in which subtour elimination
constraints are added dynamically to the formulations in a branch&cut fashion
(Padberg and Rinaldi, 1991). The CVRPSEP library by Lysgaard et al. (2004)
provides separation procedures for subtour elimination constraints, as well as
other strengthening additional inequalities. The most successful exact
solution framework is up-to-date the branch&cut&price (Desaulniers et al.,
2006; Laporte, 2009). This method is based on the Dantzig-Wolfe decomposition
(Desrosiers and Lübbecke, 2005). Binary variables model if a route is used or
not in the solution, thus their corresponding set is exponential in size. As a
consequence, a restricted set of variables is used to initiate the formulation
and only profitable routes are iteratively generated solving a subproblem,
called the pricing problem. The CVRP pricing problem is a shortest path with
resource constrains, and it is typically solved through dynamic programming
(Irnich and Desaulniers, 2005). Some of the most relevant milestones in
developing branch&cut&price algorithms for the CVRP are combining branch&cut
and column generation into the first branch&cut&price (Fukasawa et al., 2006),
applying bi-directional search in the subproblem (Righini and Salani, 2008),
introducing subset row cuts (Jepsen et al., 2008), using ng-routes to speed up
the subproblem solution (Baldacci et al., 2011), using stabilisation
techniques for dual values (Gschwind and Irnich, 2016; Pessoa et al., 2018),
and proposing primal heuristics based on the restricted master problem
(Sadykov et al., 2019). The reader might refer to Desaulniers et al. (2002)
for the most widely used acceleration techniques for the solution of the
pricing problem.

Lately, the work of Pessoa et al. (2020) provides an impressive open-source
branch&cut&price algorithm, based on Pecin et al. (2017). This algorithm
provides state-of-the-art exact solutions for the CVRP and, using a flexible
solution representation, for most of the well known routing variants and other
sequencing problems. The tool incorporates the algorithmic components
previously mentioned, as well as other recent developments (see for example,
Sadykov et al., 2021), and compares favourably to other branch&cut&price
implementations. Some of the most powerful exact algorithms for the CVRP,
available in different programming languages, are publicly available at
Sadykov (2022).

Metaheuristics are capable of solving very large CVRP instances in limited
computing time, however there is no proof of optimality for the solutions
found. They are typically initialised with solutions generated by constructive
heuristics (the Clarke and Wright is a famous example, Clarke and Wright,
1964). Metaheuristics rely heavily on local search procedures to improve the
solution quality and intensify the search, and on a metaheuristic framework to
obtain a good balance of diversification and intensification (Gendreau and
Potvin, 2010). In chronological order, popular CVRP frameworks have been the
Tabu Search (Cordeau and Laporte, 2005), the Adaptive Large Neighbourhood
Search (Pisinger and Ropke, 2007), the Iterated Local Search (Subramanian et
al., 2013), and the Hybrid Genetic algorithm (Vidal, 2022b). The latter two
examples of metaheuristic frameworks are particularly relevant to the CVRP
literature due to their high performance, their flexibility in solving
effectively many VRP variants, and because their code had been made publicly
available to the research community (the code presented in Vidal, 2022b is,
for example, available at Vidal, 2022a). Vidal et al. (2013) provide a very
good summary of the features that make a CVRP metaheuristic successful.

More recently, examples of algorithms producing very high quality solutions
for the CVRP have been:

  * •

Arnold and Sörensen (2019): data mining is used to identify solution features,
and these features are used to effectively guide the search algorithms;

  * •

Christiaens and Vanden Berghe (2020): SISRs is a ruin and recreate algorithm
based on an innovative string removal operator;

  * •

Queiroga et al. (2021): POPMUSIC is a matheuristic that iteratively solves
smaller subproblems by means of the branch&cut&price by Pessoa et al. (2020);

  * •

Accorsi and Vigo (2021): FILO is an Iterated Local Search with acceleration
techniques and annealing-based neighbour acceptance criteria;

  * •

Máximo and Nascimento (2021): AILS-PR is an Iterated Local Search
metaheuristic hybridised with Path Relinking; and,

  * •

Cavaliere et al. (2022): a refinement heuristic using a penalty-based
extension of the Lin and Kerninghan heuristic is combined with a restricted
column generation to iteratively select meaningful routes.

Clear standards have been set by the CVRP community around which benchmark
instances should be used for testing the performance of an algorithm, and
which are ways of testing a computer code for a fair comparison with other
previously proposed algorithms. Uchoa et al. (2017) discuss the most widely
used instances and provides a link to the repository, in which the input data,
as well as the best known solutions, are provided and kept up-to-date by the
authors. A more recent set of instances and best known solutions is available
in Queiroga et al. (2022), where the authors provide data enabling the use of
machine learning approaches to solve the CVRP. Accorsi et al. (2022) present
the standard practices to test CVRP algorithms: how to determine computing
time (typically on a single thread), common ways of tuning parameters, and
providing best and average solutions on a specified number of executions,
among others.

Finally, another popular and flourishing avenue for boosting research on the
development of effective solutions approaches for the CVRP and variants is
represented by competitions. Some of the most famous CVRP and routing
challenges are:

  * •

the DIMACS challenge (DIMACS, 2021), where the goal was to promote research on
challenging routing problem variants;

  * •

the Amazon Last Mile Routing Research Challenge (Amazon last mile routing,
2021), where a specific problem was tackled, namely, the challenge of
embedding driver knowledge into route optimisation;

  * •

the recently launched EURO Meets NeurIPS 2022 Vehicle Routing Competition
(EURO Meets NeurIPS 2022, 2022), with the goal of developing and comparing
machine learning techniques for the CVRP.

The Vehicle Routing problem has inspired an incredible amount of research.
This is due to the challenges it poses when it comes to solving it, to the
many variants related to it and to the relevant practical applications.
Despite the decades of research efforts and achievements, interest continues
to grow mainly thanks to the emerging topics raised by the ever changing
application environment. This chapter provides a brief, but hopefully
sufficiently comprehensive overview of the techniques, problem variants and
emerging trends which will inspire further research.

##  4 Conclusions868686This subsection was written by Daniele Vigo and Said
Salhi.

This encyclopedic article, dedicated to the 75th anniversary of the Journal of
the Operational Research Society, is made up of an Introduction and two
distinct though related sections: Methods and Applications. The introduction
section gives an interesting overview of OR with an emphasis on its origin in
the UK and highlights the methods and applications that are covered in this
paper. A brief summary of the two sections is given below.

In the first main section (§2), 24 OR-based methods are presented by experts
in their respective areas. These methods, which are given in alphabetical
order, are concisely described, each starting with the basics, then moving to
advanced and contemporary aspects. The authors also pinpoint challenging
limitations while highlighting promising research directions.

As OR is rooted in the need to solve decision problems either through
optimisation, statistics, visualisation and information technology tools, or
through soft system methodologies, we aim to retain this historical flavor in
summarising these methods by adopting a simple three-group categorisation.

The first category covers optimisation-related topics and includes 10 out of
the 24 subsections. It ranges from the original optimisation model of linear
programming (LP; §2.14) in the late 1940s to its various extensions. One is
obtained by restricting the decision variables to discrete elements including
binary ones (§2.15), allowing uncertainty in the input (§2.21), or relaxing
the objective function or the constraints not to be necessarily linear
(§2.16). An interesting area that had been dormant for more than 30 years was
revived in the late 1970s and early 1980s by studying a special case of
fractional LP which defines relative efficiency and is known as data
envelopment analysis (§2.7). Combinatorial optimisation (§2.4), a topic that
has fascinated and intrigued many mathematicians of the 18th century, seeks an
optimal subset or values from a large finite set of elements. These problems
can be defined and solved through graphs and networks (§2.12), some of which
are relatively more difficult than others. To measure the performance of
algorithms in terms of time and space complexity, computational complexity
(§2.5) emerged as a solid foundation for distinguishing between classes
defined as $\mathcal{P}$ and $\mathcal{NP}$ and studying
$\alpha$-approximation algorithms. One methodology can be traced back to the
Ancient Greek times, and is based on the ‘find and discover principle’, now
known as ‘heuristic search’ (§2.13), which has experienced a phenomenal growth
in the late 1980s and early 1990s. This is a major development since these
methodologies provide the best way to reduce not only the risk of getting
stuck at poor local optima, but also have the power to yield practical
solutions for complex discrete and global optimisation problems that could not
have been solved otherwise. A methodology that is free from restrictions of
linearity and convexity is the study of multi-stage process, as given in §2.9.

The next category includes statistics and decision-based tools and also covers
10 of the 24 subsections. For example, business analytics (§2.3), decision
analysis (§2.8) and visualisation (§2.24), though they previously existed
under different names, have grown significantly while retaining their
simplicity. Machine learning, including artificial intelligence (§2.1), which
borrowed its principles from heuristic search and statistics, has taken off
very rapidly in teaching, research and applications. This is mainly due to
computer power, sophisticated algorithms, freely available computer languages
such as R and Python, and their ability to handle massive amount of data that
are now easily available to the public. Other older topics, though still
relevant and widely applicable, have also seen a surge in new developments.
These include queueing (§2.17), forecasting (§2.10), control theory (§2.6),
and game theory (§2.11). Given the uncertainty and risk involved in many
decisions, risk analysis (§2.18) is evolving fast so as to handle such
environments alongside computer simulation (§2.19), especially discrete event
simulation. The latter, which has a wide spectrum of applications in both the
private and public sectors, has recently been enriched by incorporating multi-
objective optimisation within its evaluation component.

The last category covers the remaining four subsections. Although some of
these research areas existed in other fields such as system engineering in the
1950s, they have become contemporary OR topics especially in the UK in the
late 1970s. Soft OR and problem structuring methods (§2.20) question the
problem definition and aim to involve stakeholders for a better understanding,
with system thinking (§2.23) analysing the interactions between people,
machines and systems while also questioning the system boundaries. A related
area is system dynamics (§2.22) where the dynamism is incorporated throughout
and found to suit better applications with limited but plausible scaling. An
interesting, though relatively recent OR area, but with a long history rooted
in social psychology, is behavioural OR (§2.2), where people’s behaviour and
culture are incorporated into the decision making process. Although the
methodologies included in this category usually do not directly aim to solve
problems, they can be complementary to the harder OR techniques.

The second section covers applications that have been, since the very
beginning, strongly interconnected with the development of OR methodologies.
This section is very rich in examples coming from many fields. For the sake of
brevity, we will not refer to each subsection individually but mention just a
few. By reading the section it is evident that, on the one hand OR provides
appropriate modelling and solution tools to practical problems that arise in
the real world and are nowadays crucial in the design and management of most
systems, from healthcare and other public services, to transportation and
manufacturing. On the other hand, the complexity and size of practical
problems has always stimulated the progress of OR towards more efficient and
flexible techniques which are capable to cope with the challenges posed by the
applications. This mutual and virtuous connection is well reflected by the
richness of the Applications section of this work. It highlights not only the
traditional areas which saw tremendous research efforts and successful
implementations, as the traditional fields of transportation, manufacturing,
cutting and packing, and inventory management, but also relatively new and
interesting sectors such as sports and education.

It is worth noting that in the Applications section (§3), several dimensions
of OR impact in the real world clearly emerge. The first one is the broad
range of fields to which OR techniques have already successfully been applied
and offer an even larger potential yet to be exploited. These range from
vertical sectors, such as supply chain management, disaster relief and
recovery, or military applications, where a wide array of problems are defined
and solved through appropriate and varied methodologies, to more horizontal
domains which may impact several vertical sectors, like vehicle routing or
facility location, for which highly specialised methods have been developed.
The second dimension is related to the great variety of methodologies applied
to the different contexts. These span the whole tool set of OR, including
exact and heuristic methods developed to solve specific optimisation problems,
to techniques created to handle uncertainty and multi-criteria and, more
recently, integrating artificial intelligence methods. Indeed, the great
improvements achieved in the last decades in integer and nonlinear programming
now allow to effectively model and solve many problems arising at the
operational and tactical levels, where data are more available and reliable.
The uncertainty in the data and the modelling typical of strategic decisions
are successfully handled by a variety of methodologies that have proven to be
effective in the solution of real applications which are well reviewed in this
work. A third very interesting dimension is represented by the development of
new broad research perspectives which may have a strong impact in all fields
of OR and are deeply motivated by applications. An excellent example is the
inclusion of fairness and ethics in optimisation which, on the one hand allow
for considering important issues favouring the acceptability and usability of
the results, and on the other hand pose new methodological challenges.

As a general conclusion, thanks to the advances in computer technology, the
availability of massive amount of live data, and novel developments, in both
optimisation and statistics, effective optimisation software, powerful machine
learning techniques and visualisation tools now exist to solve problems that
were considered practically unsolvable just a decade ago. Applications have
always been a main driver for OR development, and the successes achieved
increase the appetite for further improvements.

In the more classical area of exact and heuristic techniques, there is clearly
a need to improve the capability of handling efficiently large and very large-
scale instances to cope with more complex and demanding scenarios. This
increase in scale is not only generated by the need to solve larger problems,
but also to incorporate various steps of the planning processes into
integrated and more comprehensive methods. A field that still deserves further
research efforts is the consideration of uncertainty in OR methods. Important
methodological obstacles have yet to be surmounted and there is clearly a need
for the development of simple and pragmatic methods, possibly resulting from
the integration of artificial intelligence techniques, which can be applied to
the solution of large-scale problems arising in several important application
domains. However, it is also worth stressing that these advances, though they
are welcome, may suffer from shortcomings, such as the local optimality trap,
biased data, and impractical assumptions. These hidden aspects could yield
poor outcomes on which academics and practitioners ought to keep an open eye.

## Disclaimer

The views expressed in this paper are those of the authors and do not
necessarily reflect the views of their affiliated institutions and
organisations.

## Acknowledgements

Fotios Petropoulos would like to thank all the co-authors of this article for
their very enthusiastic response and participation in this initiave. He is
also indebted to his lead advisor for this project, Gilbert Laporte, as well
as Christos Vasilakis, Güneş Erdoğan, Stephen Disney and Maria Battarra for
their help and suggestions. Finally, Fotios is grateful to John Boylan and the
other Editors-in-Chief of the Journal of the Operational Research Society for
inviting this paper to be part of the 75th issue of the journal. Fotios
dedicates this article to Professor John Boylan: John, your kindness will
always be remembered.

Maria Battarra’s work reported in this paper was undertaken as part of the
Made Smarter Innovation: Centre for People-Led Digitalisation, at the
University of Bath, University of Nottingham, and Loughborough University.

David Canca’s work was supported by the University of Sevilla, the Regional
Government of Andalucia (Spain) and the European Regional Development Fund
(ERDF) under grant US-1381656.

Laurent Charlin and Andrea Lodi would like to thank Didier Chételat and Mizu
Nishikawa-Toomey for reading and commenting on drafts of their subsection
(§2.1) and the CIFAR AI Chair and the CERC programs for funding.

Salvatore Greco wishes to acknowledge the support of the Ministero
dell’Istruzione, dell’Universita e dellaRicerca (MIUR) - PRIN 2017, project
“Multiple Criteria Decision Analysis and Multiple Criteria Decision Theory”,
grant 2017CY2NCA.

Katherine Kent and Sam Rose thank Mithu Norris for the help and coordination
with §3.10 but also Emma Hickman and Ffion Lelii for their contribution.

Silvano Martello, Paolo Toth and Daniele Vigo were supported by Air Force
Office of Scientific Research under Grants no. FA8655-20-1-7012,
FA8655-20-1-7019, FA9550-17-1-0234 and FA8655-21-1-7046.

Dimitrios Sotiros’s work was partially supported by the National Science
Center (NCN, Poland) grant no. 2020/37/B/HS4/03125.

Greet Vanden Berghe and Sanja Petrovic acknowledge the advice provided by
Andrea Schaerf (University of Udine).

Rafał Weron’s work was partially supported by the National Science Center
(NCN, Poland) grant no. 2018/30/A/HS4/00444.

##  Appendix A List of acronyms

1D: One-Dimensional
2D: Two-Dimensional
2DKP: Two-Dimensional Knapsack Problem
2S-SPR: Two-Stage Stochastic Programming with Recourse
3D: Three-Dimensional
ABM: Agent Based Modelling
ABS: Agent Based Simulation
ADP: Approximate Dynamic Programming
AFS: Airport Flight Scheduling
AHD: Attended Home Delivery
AHP: Analytic Hierarchy Process
AI: Artificial Intelligence
ANN: Artificial Neural Network
ANT: Actor Network Theory
AoA: Activity-on-Arc
AoN: Activity-on-Node
AOR: Airline Operations Recovery
AP: Assignment Problem
ARIMA: AutoRegressive Integrated Moving Average (model)
AR: Assurance Region
AR: Action Research
ARIMAX: AutoRegressive Integrated Moving Average with eXogenous variables
(model)
AS: Autonomous System
ASP: Airline Schedule Planning
ATFM: Air Traffic Flow Management
B2B: Business-To-Business
B2C: Business-To-Consumer
B&B: Branch-and-Bound
B&C: Branch-and-Cut
B&P: Branch-and-Price
BN: Bayesian Network
BOR: Behavioural OR
BPP: Bin Packing Problem
C&P: Cutting and Packing
CBOR: Community-Based Operations Research
CDEA: Centralised DEA
CDM: Central Decision Maker or Collaborative Decision-Making
CLD: Causal Loop Diagram
CLSC: Closed-Loop Supply Chains
CM: Cellular Manufacturing
CNN: Convolutional Neural Network
CO: Combinatorial Optimisation
CODP: Customer Order decoupling point
ConFL: Connected Facility Location Problem
COR: Community Operational Research
CPM: Critical Path Method
CRPS: Continuous Ranked Probability Score
CST: Critical Systems Thinking
CSW: Common Set of Weights
CVaR: Conditional Value at Risk
CVRP: Capacitated Vehicle Routing Problem
DBN: Dynamic Bayesian Network
DC: Distribution Centre
DCT: Daily Contact Testing
DDF: Directional Distance Function
DEA: Data Envelopment Analysis
DEF: Deterministic Equivalent Formulation
DES: Discrete Event Simulation
DfT: Department for Transport
DHSC: Department of Health and Social Care
DMU: Decision Making Unit
DNDEA: Dynamic Network DEA
DNN: Deep Neural Network
DP: Dynamic Programming
DPSIR: Drivers, Pressures, State, Impact and Response
DS: Data Science
DSS: Decision Support Systems
EAT: Efficiency Analysis Trees
ED: Emergency Department
EMSR: Expected Marginal Seat Revenue
EOQ: Economic Order Quantity
ERP: Enterprise Resource Planning
ESICUP: EURO Special Interest Group on Cutting and Packing
EURO: European Operational Research Societies
EVP: Expected Value of Possession
FAM: Fleet Assignment Model
FIFO: First-In-First-Out
FMS: Flexible Manufacturing Systems
FPTAS: Fully Polynomial-Time Approximation Scheme
FSF: Full-State Feedback
FSO: Fixed-sum output
FTU: Facilities-Transformation-Usage (framework)
GIS: Geographic Information Systems
GLM: Generalised Linear Model
GMB: Group Model Building
GNN: Graphical Neural Network
GORS: Government Operational Research Service
GP: Gaussian Process
GPS: Global Positioning System
GPU: Graphics Processing Unit
GRASP: Greedy Randomised Adaptive Search Procedure
HJB: Hamilton-Jacobi-Bellman
HMT: His Majesty’s Treasury
HL: Humanitarian Logistics
HORAF: Heads of OR and Analytics Forum
IAM: Integrated Assessment Model
ICU: Intensive Care Unit
IGP: Interior Gateway Protocol
IHIP: Intangibility, Heterogeneity, Inseparability, and Perishability
IID: Independently and Identically Distributed
INFORMS: Institute for Management Science and Operations Research
INRC: International Nurse Rostering Competition
ILP: Integer Linear Problem
ILP: Integer Linear Programming
IoT: Internet of Things
IP: Integer Programming
IRP: Inventory-Routing Problem
JIT-MS: Just-In-Time Material System
KP: Knapsack Problem
LASSO: Least Absolute Shrinkage and Selection Operator
LCSA: Life Cycle Sustainability Assessment
LEAR: LASSO-Estimated AutoRegressive (model)
LP: Linear Programming
LQG: Linear Quadratic Gaussian
MAE: Mean Absolute Error
MAPE: Mean Absolute Percentage Error
MASE: Mean Absolute Scaled Error
MAUT: Multi-Attribute Utility Theory
MAVT: Multi-Attribute Value Theory
MBM: Market Based Measure
MC: Maximum Clique or Minimum Cut (problem)
MCDA: Multi-Criteria Decision Analysis
MCF: Minimum Cost Flow (problem)
MDP: Markov Decision Process
MF: Maximum Flow (problem)
MILP: Mixed-Integer Linear Programming
MINLP: Mixed-Integer NonLinear Programming
MIMO: Multi-Input-Multi-Output
MIP: Mixed-Integer Programming
ML: Machine Learning
MLPI: Malmquist Luenberger Productivity Indicator
MPC: Model Predictive Control
MPI: Malmquist Productivity Index
MRP: Material Requirement Planning
MRP: Multi-level Regression Post-stratification
NBEATS: Neural Basis Expansion Analysis for interpretable Time Series
forecasting
NDEA: Network DEA
NDP: Neural Dynamic Programming
NFL: National Football League
NHS: National Health Service
NGO: Non-Governmental Organisation
OM: Operations Management
ONS: Office for National Statistics
OR: Operational (or Operations) Research
PA: Portfolio Analysis
PATAT: Practice and Theory of Automated Timetabling
PCR: Polymerase Chain Reaction
PERT: Project Evaluation and Review Technique
PESP: Periodic Event Scheduling Problem
PID: Proportional Integral Derivative
POMDP: Partially Observable Markov Decision Process
PPS: Production Possibility Set
PRA: Probabilistic Risk Assessment
PSM: Problem Structuring Method
PTAS: Polynomial-Time Approximation Scheme
QRA: Quantile Regression Averaging or Quantitative Risk Assessment
R&D: Research and Development
RCPSP: Resource-Constrained Project Scheduling Problem
RES: Renewable Energy Sources
RFID: Radio-Frequency IDentification
RINS: Relaxation-Induced Neighbourhood Search
RL: Reinforcement Learning or Reverse Logistics
RM: Revenue Management
RMSE: Root Mean Squared Error
RNN: Recurrent Neural Network
SAA: Sample Average Approximation
SARF: Social Amplification of Risk Framework
SAT: SATisfiability (problem)
SCA: Strategic Choice Approach
SCM: Supply Chain Management
SD: Systems Dynamics
SDM: Structured Decision Making
SFA: Stochastic Frontier Analysis
SI: Systemic Intervention
SIS: Schools Infection Survey
SISO: Single-Input-Single-Output
SODA: Strategic Options Development and Analysis
SR: Segment Routing
SRCPSP: Stochastic Resource-Constrained Project Scheduling Problem
SSM: Soft Systems Methodology
SST: Shortest Spanning Trees
STP: Steiner Tree Problem (in graphs)
SVF: Support Vector Frontiers
SVM: Support Vector Machine
TE: Traffic Engineering
TFP: Total Factor Productivity
TPS: Toyota Production System
TSP: Travelling Salesman Problem
TTP: Travelling Tournament Problem
UDE: UnDesirable Effects
UFLP: Uncapacitated Facility Location Problem
VaR: Value at Risk
VAR: Vector AutoRegressive (model)
VMI: Vendor Managed Inventory
VPP: Virtual Power Plant
VRP: Vehicle Routing Problems
VSM: Viable Systems Model or Value Stream Map
VSS: Value of Stochastic Solution
VUCA: Volatile, Uncertain, Complex and Ambiguous
WHO: World Health Organisation

## References

  * Aardal et al. (2000) Aardal, K., Bixby, R., Hurkens, C., Lenstra, A., Smeltink, J., 2000. Market split and basis reduction: Towards a solution of the Cornuéjols-Dawande instances. INFORMS Journal on Computing 12, 192–202.
  * Abadi et al. (2015) Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.
  * Abbas et al. (2017) Abbas, A. E., Tambe, M., von Winterfeldt, D., 2017. Improving Homeland Security Decisions. Cambridge University Press, Cambridge.
  * Abbey et al. (2015) Abbey, J. D., Blackburn, J., Guide, V. D. R., 2015. Optimal pricing for new and remanufactured products. Journal of Operations Management 36, 130–146.
  * Abbey et al. (2017) Abbey, J. D., Kleber, R., Souza, G. C., Voigt, G., 2017. The role of perceived quality risk in pricing remanufactured products. Production and Operations Management 26 (1), 100–115.
  * Abbott and Doucouliagos (2009) Abbott, M., Doucouliagos, C., 2009. Competition and efficiency: overseas students and technical efficiency in Australian and New Zealand universities. Education Economics 17 (1), 31–57.
  * Accorsi et al. (2022) Accorsi, L., Lodi, A., Vigo, D., 2022. Guidelines for the computational testing of machine learning approaches to vehicle routing problems. Operations Research Letters 50 (2), 229–234.
  * Accorsi and Vigo (2021) Accorsi, L., Vigo, D., 2021. A fast and scalable heuristic for the solution of large-scale capacitated vehicle routing problems. Transportation Science 55 (4), 832–856.
  * Acito and Khatri (2014) Acito, F., Khatri, V., 2014. Business analytics: Why now and what next? Business Horizons 57 (5), 565–570.
  * Ackermann and Eden (2011) Ackermann, F., Eden, C., 2011. Making Strategy: Mapping Out Strategic Success, 2nd Edition. SAGE, London.
  * Ackermann and Howick (2022) Ackermann, F., Howick, S., 2022. Experiences of mixed method or practitioners: Moving beyond a technical focus to insights relating to modelling teams. Journal of the Operational Research Society 73 (9), 1905–1918.
  * Ackoff (1956) Ackoff, R. L., 1956. The development of operations research as a science. Operations Research 4 (3), 265–295.
  * Ackoff (1970) Ackoff, R. L., 1970. A black ghetto’s research on a university. Operations Research 18 (5), 761–771.
  * Ackoff (1977) Ackoff, R. L., 1977. Optimization + objectivity = optout. European Journal of Operational Research 1 (1), 1–7.
  * Ackoff (1979a) Ackoff, R. L., 1979a. The future of operational research is past. Journal of the Operational Research Society 30 (2), 93–104.
  * Ackoff (1979b) Ackoff, R. L., 1979b. Resurrecting the future of operational research. Journal of the Operational Research Society 30 (3), 189–199.
  * Ackoff (1981) Ackoff, R. L., 1981. Creating the Corporate Future: Plan Or be Planned For. Wiley.
  * Acworth et al. (1998) Acworth, P. A., Broadie, M., Glasserman, P., 1998. A comparison of some Monte Carlo and quasi Monte Carlo techniques for option pricing. In: Niederreiter, H., Hellekalek, P., Larcher, G., Zinterhof, P. (Eds.), Monte Carlo and Quasi-Monte Carlo Methods 1996. Springer-Verlag, New York, NY, pp. 1–18.
  * Adouani et al. (2022) Adouani, Y., Jarboui, B., Masmoudi, M., 2022. A matheuristic for the 0–1 generalized quadratic multiple knapsack problem. Optimization Letters 16, 37–58.
  * Afsharian and Podinovski (2018) Afsharian, M., Podinovski, V. V., 2018. A linear programming approach to efficiency evaluation in nonconvex metatechnologies. European Journal of Operational Research 268 (1), 268–280.
  * Afzali et al. (2012) Afzali, H. H. A., Karnon, J., Gray, J., 2012. A critical review of model-based economic studies of depression: modelling techniques, model structure and data sources. PharmacoEconomics 30 (6), 461–482.
  * Agarwal and Ergun (2008) Agarwal, R., Ergun, Ö., 2008. Ship scheduling and network design for cargo routing in liner shipping. Transportation Science 42 (2), 175–196.
  * Agasisti (2016) Agasisti, T., 2016. Cost structure, productivity and efficiency of the Italian public higher education industry 2001–2011. International Review of Applied Economics 30 (1), 48–68.
  * Agatz et al. (2011) Agatz, N., Campbell, A., Fleischmann, M., Savelsbergh, M., 2011. Time slot management in attended home delivery. Transportation Science 45 (3), 435–449.
  * Agatz et al. (2013) Agatz, N., Campbell, A. M., Fleischmann, M., Van Nunen, J., Savelsbergh, M., 2013\. Revenue management opportunities for internet retailers. Journal of Revenue and Pricing Management 12 (2), 128–138.
  * Agatz et al. (2008) Agatz, N., Fleischmann, M., Van Nunen, J. A., 2008. E-fulfillment and multi-channel distribution–a review. European Journal of Operational Research 187 (2), 339–356.
  * Aghezzaf (2005) Aghezzaf, E., 2005. Capacity planning and warehouse location in supply chains with uncertain demands. Journal of the Operational Research Society 56 (4), 453–462.
  * Agrawal et al. (2019) Agrawal, S., Avadhanula, V., Goyal, V., Zeevi, A., 2019. MNL-bandit: A dynamic learning approach to assortment selection. Operations Research 67 (5), 1453–1485.
  * Ahuja et al. (1993) Ahuja, R. K., Magnanti, T. L., Orlin, J. B., 1993. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, Englewood Cliffs, NJ.
  * Aigner et al. (1977) Aigner, D., Lovell, C. A. K., Schmidt, P., 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6 (1), 21–37.
  * AIMMS (2022) AIMMS, 2022. AIMMS.
URL https://www.aimms.com/

  * Akçalı et al. (2009) Akçalı, E., Çetinkaya, S., Üster, H., 2009. Network design for reverse and closed-loop supply chains: An annotated bibliography of models and solution approaches. Networks 53 (3), 231–248.
  * Akhmedov et al. (2016) Akhmedov, M., Kwee, I., Montemanni, R., 2016. A divide and conquer matheuristic algorithm for the prize-collecting steiner tree problem. Computers & Operations Research 70, 18–25.
  * Akhtar et al. (2015) Akhtar, S., Scarf, P., Rasool, Z., 2015. Rating players in test match cricket. Journal of the Operational Research Society 66 (4), 684–695.
  * Al Hajj Hassan et al. (2022) Al Hajj Hassan, L., Hewitt, M., Mahmassani, H. S., 2022. Daily load planning under different autonomous truck deployment scenarios. Transportation Research Part E: Logistics and Transportation Review 166, 102885.
  * Al-Kanj et al. (2020) Al-Kanj, L., Nascimento, J., Powell, W. B., 2020. Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles. European Journal of Operational Research 284 (3), 1088–1106.
  * Albano and Sapuppo (1980) Albano, A., Sapuppo, G., 1980. Optimal allocation of Two-Dimensional irregular shapes using heuristic search methods. IEEE Transactions on Systems, Man, and Cybernetics 10 (5), 242–248.
  * Albayrak Ünal et al. (2023) Albayrak Ünal, Ö., Erkayman, B., Usanmaz, B., 2023. Applications of artificial intelligence in inventory management: A systematic review of the literature. Archives of Computational Methods in Engineering 30 (4), 2605–2625.
  * Alexander (1992) Alexander, C., 1992. The Kalai–Smorodinsky bargaining solution in wage negotiations. Journal of the Operational Research Society 43, 779–786.
  * Alfandari et al. (2022) Alfandari, L., Ljubić, I., da Silva, M. D. M., 2022. A tailored benders decomposition approach for last-mile delivery with autonomous robots. European Journal of Operational Research 299 (2), 510–525.
  * Alfieri et al. (2006) Alfieri, A., Groot, R., Kroon, L., Schrijver, A., 2006. Efficient circulation of railway rolling stock. Transportation Science 40 (3), 378–391.
  * Algaba et al. (2020) Algaba, A., Ardia, D., Bluteau, K., Borms, S., Boudt, K., 2020. Econometrics meets sentiment: An overview of methodology and applications. Journal of Economic Surveys 34 (3), 512–547.
  * Ali et al. (2022) Ali, S., Ramos, A. G., Carravilla, M. A., Oliveira, J. F., 2022. On-line three-dimensional packing problems: A review of off-line and on-line solution approaches. Computers & Industrial Engineering 168, 108122.
  * Allen et al. (1997) Allen, R., Athanassopoulos, A., Dyson, R. G., Thanassoulis, E., 1997. Weights restrictions and value judgements in data envelopment analysis: Evolution, development and future directions. Annals of Operations Research 73, 13–34.
  * Allon and Van Mieghem (2010) Allon, G., Van Mieghem, J. A., 2010. Global dual sourcing: Tailored base-surge allocation to near- and offshore production. Management Science 56 (1), 110–124.
  * Almgren and Chriss (2001) Almgren, R., Chriss, N., 2001. Optimal execution of portfolio transactions. Journal of Risk 3, 5–40.
  * Altay and Green III (2006) Altay, N., Green III, W. G., 2006. OR/MS research in disaster operations management. European Journal of Operational Research 175 (1), 475–493.
  * Altay and Labonte (2014) Altay, N., Labonte, M., 2014. Challenges in humanitarian information management and exchange: evidence from Haiti. Disasters 38 (s1), S50–S72.
  * Altın et al. (2013) Altın, A., Fortz, B., Thorup, M., Ümit, H., 2013. Intra-domain traffic engineering with shortest path routing protocols. Annals of Operations Research 204 (1), 65–95.
  * Alumur and Kara (2008) Alumur, S., Kara, B. Y., 2008. Network hub location problems: The state of the art. European Journal of Operational Research 190, 1–21.
  * Alumur et al. (2021) Alumur, S. A., Campbell, J. F., Contreras, I., Kara, B. Y., Marianov, V., O’Kelly, M. E., 2021. Perspectives on modeling hub location problems. European Journal of Operational Research 291 (1), 1–17.
  * Alvarez et al. (2011) Alvarez, J. F., Tsilingiris, P., Engebrethsen, E. S., Kakalis, N. M. P., 2011. Robust fleet sizing and deployment for industrial and independent bulk ocean shipping companies. INFOR: Information Systems and Operational Research 49 (2), 93–107.
  * Álvarez-Miranda et al. (2020) Álvarez-Miranda, E., Salgado-Rojas, J., Hermoso, V., Garcia-Gonzalo, J., Weintraub, A., 2020. An integer programming method for the design of multi-criteria multi-action conservation plans. Omega 92, 102147.
  * Alvarez-Valdes et al. (2013) Alvarez-Valdes, R., Martinez, A., Tamarit, J. M., 2013. A branch & bound algorithm for cutting and packing irregularly shaped pieces. International Journal of Production Economics 145 (2), 463–477.
  * Alyahyan and Düstegör (2020) Alyahyan, E., Düstegör, D., 2020. Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education 17, 3.
  * Amazon last mile routing (2021) Amazon last mile routing, 2021. Research challenge. https://routingchallenge.mit.edu/, accessed on 2021-09-14.
  * Anandalingam (1987) Anandalingam, G., 1987. Asymmetric players and bargaining for profit shares in natural resource development. Management Science 33 (8), 1048–1057.
  * Andersen et al. (2007) Andersen, D. F., Vennix, J. A. M., Richardson, G. P., Rouwette, E. A. J. A., 2007\. Group model building: problem structuring, policy simulation and decision support. Journal of the Operational Research Society 58 (5), 691–694.
  * Anderson et al. (2017) Anderson, E., Chen, B., Shao, L., 2017. Supplier competition with option contracts for discrete blocks of capacity. Operations Research 65 (4), 952–967.
  * Anderson et al. (2022) Anderson, E., Chen, B., Shao, L., 2022. Capacity games with supply function competition. Operations Research 70 (4), 1969–1983.
  * Andersson et al. (2001) Andersson, F., Mausser, H., Rosen, D., Uryasev, S., 2001. Credit risk optimization with conditional value-at-risk criterion. Mathematical Programming 89 (2), 273–291.
  * Andersson et al. (2011) Andersson, H., Duesund, J. M., Fagerholt, K., 2011. Ship routing and scheduling with cargo coupling and synchronization constraints. Computers & Industrial Engineering 61 (4), 1107–1116.
  * Andersson et al. (2015) Andersson, H., Fagerholt, K., Hobbesland, K., 2015. Integrated maritime fleet deployment and speed optimization: Case study from RoRo shipping. Computers & Operations Research 55, 233–240.
  * Andras (2010) Andras, V., 2010. Omnet++. In: Klaus, W., Mesut, G., James, G. (Eds.), Modeling and Tools for Network Simulation. Springer, Berlin, Heidelberg, pp. 35–59.
  * Angelelli et al. (2010) Angelelli, E., Mansini, R., Speranza, M. G., 2010. Kernel search: A general heuristic for the multi-dimensional knapsack problem. Computers & Operations Research 37 (11), 2017–2026.
  * Angelus (2023) Angelus, A., 2023. Generalizations of the clark-scarf model and analysis. In: Song, J.-S. (Ed.), Research Handbook on Inventory Management. Edward Elgar Publishing.
  * Annabi et al. (2012) Annabi, A., Breton, M., François, P., 2012. Resolution of financial distress under chapter 11. Journal of Economic Dynamics and Control 36 (12), 1867–1887.
  * Antunes et al. (2019) Antunes, D., Vaze, V., Antunes, A. P., 2019. A robust pairing model for airline crew scheduling. Transportation Science 53 (6), 1751–1771.
  * Aparicio et al. (2017) Aparicio, J., Crespo-Cebada, E., Pedraja-Chaparro, F., Santín, D., 2017. Comparing school ownership performance using a pseudo-panel database: A Malmquist-type index approach. European Journal of Operational Research 256 (2), 533–542.
  * Aparicio et al. (2007) Aparicio, J., Ruiz, J. L., Sirvent, I., 2007. Closest targets and minimum distance to the Pareto-efficient frontier in DEA. Journal of Productivity Analysis 28 (3), 209–218.
  * Applegate et al. (2007) Applegate, D., Bixby, R., Chvátal, V., Cook, W., 2007. The Traveling Salesman Problem: A Computational Study. Princeton University Press, Princeton.
  * Applegate et al. (2011) Applegate, D. L., Bixby, R. E., Chvátal, V., Cook, W. J., 2011. The Traveling Salesman Problem. Princeton University Press, Princeton.
  * Arana-Jiménez et al. (2022) Arana-Jiménez, M., Sánchez-Gil, M. C., Lozano, S., Younesi, A., 2022. Efficiency assessment using fuzzy production possibility set and enhanced Russell Graph measure. Computational and Applied Mathematics 41 (2), 79.
  * Araújo et al. (2020) Araújo, A. M., Santos, D., Marques, I., Barbosa-Povoa, A., 2020. Blood supply chain: a two-stage approach for tactical and operational planning. OR Spectrum 43, 1023–1053.
  * Archetti and Bertazzi (2021) Archetti, C., Bertazzi, L., 2021. Recent challenges in routing and inventory routing: E-commerce and last-mile delivery. Networks 77 (2), 255–268.
  * Archetti et al. (2007) Archetti, C., Bertazzi, L., Laporte, G., Speranza, M. G., 2007. A branch-and-cut algorithm for a vendor-managed inventory-routing problem. Transportation Science 41 (3), 382–391.
  * Archetti et al. (2017) Archetti, C., Boland, N., Speranza, M. G., 2017. A matheuristic for the multivehicle inventory routing problem. INFORMS Journal on Computing 29 (3), 377–387.
  * Archetti et al. (2015) Archetti, C., Corberán, A., Plana, I., Sanchis, J. M., Speranza, M. G., 2015\. A matheuristic for the team orienteering arc routing problem. European Journal of Operational Research 245, 392–401.
  * Argyris et al. (2022) Argyris, N., Karsu, Ö., Yavuz, M., 2022. Fair resource allocation: Using welfare-based dominance constraints. European Journal of Operational Research 297 (2), 560–578.
  * Ariely and Simonson (2003) Ariely, D., Simonson, I., 2003. Buying, bidding, playing, or competing? value assessment and decision dynamics in online auctions. Journal of consumer psychology: the official journal of the Society for Consumer Psychology 13 (1), 113–123.
  * Aringhieri et al. (2017) Aringhieri, R., Bruni, M. E., Khodaparasti, S., van Essen, J. T., 2017. Emergency medical services and beyond: Addressing new challenges through a wide literature review. Computers & Operations Research 78, 349–368.
  * Arnold and Sörensen (2019) Arnold, F., Sörensen, K., 2019. What makes a VRP solution good? The generation of problem-specific knowledge for heuristics. Computers & Operations Research 106, 280–288.
  * Arnold et al. (2004) Arnold, P., Peeters, D., Thomas, I., 2004. Modelling a rail/road intermodal transportation system. Transportation Research Part E: Logistics and Transportation Review 40 (3), 255–270.
  * Arrow et al. (1951) Arrow, K. J., Harris, T., Marschak, J., 1951. Optimal inventory policy. Econometrica 19 (3), 250–272.
  * Arrow et al. (1958) Arrow, K. J., Karlin, S., Scarf, H. E., 1958. Studies in the mathematical theory of inventory and production. Stanford University Press.
  * Artzner et al. (1999) Artzner, P., Delbaen, F., Eber, J.-M., Heath, D., 1999. Coherent measures of risk. Mathematical Finance 9 (3), 203–228.
  * Arvan et al. (2019) Arvan, M., Fahimnia, B., Reisi, M., Siemsen, E., 2019. Integrating human judgement into quantitative forecasting methods: A review. Omega 86, 237–252.
  * Asghari et al. (2023) Asghari, M., Jaber, M. Y., Mirzapour Al-e-hashem, S. M. J., 2023. Coordinating vessel recovery actions: Analysis of disruption management in a liner shipping service. European Journal of Operational Research 307 (2), 627–644, , DOI: 10.1016/j.ejor.2022.08.039.
  * Asmuni et al. (2009) Asmuni, H., Burke, E. K., Garibaldi, J. M., McCollum, B., Parkes, A. J., 2009. An investigation of fuzzy multiple heuristic orderings in the construction of university examination timetables. Computers & Operations Research 36 (4), 981–1001.
  * Åström (2012) Åström, K. J., 2012. Introduction to Stochastic Control Theory. Courier Corporation.
  * Åström and Kumar (2014) Åström, K. J., Kumar, P. R., 2014. Control: A perspective. Automatica 50 (1), 3–43.
  * Åström and Wittenmark (2013) Åström, K. J., Wittenmark, B., 2013. Adaptive control. Courier Corporation.
  * Atan et al. (2017) Atan, Z., Ahmadi, T., Stegehuis, C., de Kok, T., Adan, I., 2017. Assemble-to-order systems: A review. European Journal of Operational Research 261 (3), 866–879.
  * Atasu et al. (2008) Atasu, A., Sarvary, M., Van Wassenhove, L. N., 2008. Remanufacturing as a marketing strategy. Management Science 54 (10), 1731–1746.
  * Athanasopoulos et al. (2020) Athanasopoulos, G., Gamakumara, P., Panagiotelis, A., Hyndman, R. J., Affan, M., 2020. Hierarchical forecasting. In: Fuleky, P. (Ed.), Macroeconomic Forecasting in the Era of Big Data: Theory and Practice. Springer, pp. 689–719.
  * Atkinson and Gary (2016) Atkinson, S., Gary, M. S., 2016. Mergers and acquisitions: Modeling decision making in integration projects. In: Kunc, M., Malpass, J., White, L. (Eds.), Behavioral Operational Research: Theory, Methodology and Practice. Palgrave Macmillan, London, pp. 319–336.
  * Ausiello et al. (1999) Ausiello, G., Crescenzi, P., Gambosi, G., Kann, V., Marchetti-Spaccamela, A., Protasi, M., 1999. Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties. Springer, Berlin.
  * Aven (2015) Aven, T., 2015. Risk Analysis. John Wiley & Sons.
  * Aven (2020) Aven, T., 2020. Three influential risk foundation papers from the 80s and 90s: Are they still state-of-the-art? Reliability Engineering & System Safety 193, 106680.
  * Aviv (2003) Aviv, Y., 2003. A time-series framework for supply-chain inventory management. Operations Research 51 (2), 210–227.
  * Axsäter (1993) Axsäter, S., 1993. Continuous review policies for multi-level inventory systems with stochastic demand. Handbooks in Operations Research and Management Science 4, 175–197.
  * Axsäter (1996) Axsäter, S., 1996. Using the deterministic eoq formula in stochastic inventory control. Management Science 42 (6), 830–834.
  * Axsäter (2003) Axsäter, S., 2003. Supply chain operations: Serial and distribution inventory systems. In: Graves, S. C., de Kok, A. G. (Eds.), Handbooks in Operations Research and Management Science. Vol. 11. Elsevier, pp. 525–559.
  * Axsäter (2006) Axsäter, S., 2006. Inventory Control. Springer.
  * Axsäter and Rosling (1993) Axsäter, S., Rosling, K., 1993. Installation vs. echelon stock policies for multilevel inventory control. Management Science 39 (10), 1274–1280.
  * Ayhan and Baccelli (2001) Ayhan, H., Baccelli, F., 2001. Expansions for joint laplace transform of stationary waiting times in (max,+)-linear systems with poisson input. Queueing Systems 37 (1), 291–328.
  * Ayhan et al. (2004) Ayhan, H., Palmowski, Z., Schegel, S., 2004. Cyclic queueing networks with subexponential service trimes. Journal of Applied Probability 41 (3), 291–301.
  * Azoury (1985) Azoury, K. S., 1985. Bayes solution to dynamic inventory models under unknown demand distribution. Management Science 31 (9), 1150–1160.
  * Baar et al. (1999) Baar, T., Brucker, P., Knust, S., 1999. Tabu search algorithms and lower bounds for the Resource-Constrained project scheduling problem. In: Voß, S., Martello, S., Osman, I. H., Roucairol, C. (Eds.), Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Springer US, Boston, MA, pp. 1–18.
  * Babich et al. (2021) Babich, V., Birge, J. R., et al., 2021. The interface of finance, operations, and risk management. Foundations and Trends® in Technology, Information and Operations Management 15 (1–2), 1–203.
  * Babich and Hilary (2020) Babich, V., Hilary, G., 2020. OM Forum—Distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manufacturing & Service Operations Management 22 (2), 223–240.
  * Baboolal et al. (2012) Baboolal, K., Griffiths, J. D., Knight, V. A., Nelson, A. V., Voake, C., Williams, J. E., 2012. How efficient can an emergency unit be? A perfect world model. Emergency Medicine Journal 29 (12), 972–977.
  * Baccelli et al. (1992) Baccelli, F., Cohen, G., Olsder, G. J., Quadrat, J.-P., 1992. Synchronization and linearity: an algebra for discrete event systems. John Wiley & Sons Ltd.
  * Baccelli et al. (1997) Baccelli, F., Hasenfuss, S., Schmidt, V., 1997. Transient and stationary waiting times in (max,+)-linear systems with poisson input. Queueing Systems 26 (3), 301–342.
  * Baccelli et al. (1999) Baccelli, F., Schlegel, S., Schmidt, V., 1999. Asymptotics of stochastic networks with subexponential service times. Queueing Systems 33, 205–232.
  * Baccelli and Schmidt (1996) Baccelli, F., Schmidt, V., 1996. Taylor series expansions for poisson-driven $(max,+)$-linear systems. The Annals of Applied Probability 6 (1), 138–185.
  * Bacciotti and Rosier (2005) Bacciotti, A., Rosier, L., 2005. Liapunov functions and stability in control theory. Springer Science & Business Media.
  * Başar et al. (2018) Başar, T., Haurie, A., Zaccour, G., 2018. Nonzero-sum differential games. In: Başar, T., Zaccour, G. (Eds.), Handbook of Dynamic Game Theory. Springer, Cham, pp. 61–110.
  * Başar and Olsder (1999) Başar, T., Olsder, G., 1999. Dynamic noncooperative game theory, 2nd Edition. SIAM, Philadelphia.
  * Bailey (1952) Bailey, N. T. J., 1952. A study of queues and appointment systems in hospital out-patient departments, with special reference to waiting-times. Journal of the Royal Statistical Society 14 (2), 185–199.
  * Baker and McHale (2015) Baker, R. D., McHale, I. G., 2015. Deterministic evolution of strength in multiple comparisons models: Who is the greatest golfer? Scandinavian Journal of Statistics, Theory and Applications 42 (1), 180–196.
  * Bakir et al. (2021) Bakir, I., Erera, A., Savelsbergh, M., 2021. Motor carrier service network design. In: Crainic, T. G., Gendreau, M., Gendron, B. (Eds.), Network Design with Applications to Transportation and Logistics. Springer, pp. 427–467.
  * Bakshi and Pinker (2018) Bakshi, N., Pinker, E., 2018. Public warnings in counterterrorism operations: Managing the “Cry-Wolf” effect when facing a strategic adversary. Operations Research 66 (4), 977–993.
  * Balakrishnan and Altinkemer (1992) Balakrishnan, A., Altinkemer, K., 1992. Using a hop-constrained model to generate alternative communication network design. ORSA Journal on Computing 4 (2), 192–205.
  * Balas (1965) Balas, E., 1965. An additive algorithm for solving linear programs with zero-one variables. Operations Research 13, 517–546.
  * Balas (1971) Balas, E., 1971. Intersection cuts—a new type of cutting planes for integer programming. Operations Research 19, 19–39.
  * Balas (1975) Balas, E., 1975. Facets of the knapsack polytope. Mathematical Programming 8, 146–164.
  * Balas (1979) Balas, E., 1979. Disjunctive programming. Annals of Discrete Mathematics 5, 3–51.
  * Balas et al. (1993) Balas, E., Ceria, S., Cornuéjols, G., 1993. A lift-and-project cutting plane algorithm for mixed 0–1 programs. Mathematical Programming 58, 295–324.
  * Balas et al. (1996a) Balas, E., Ceria, S., Cornuéjols, G., 1996a. Mixed 0-1 programming by lift-and-project in a branch-and-cut framework. Management Science 42, 1229–1246.
  * Balas et al. (1996b) Balas, E., Ceria, S., Cornuéjols, G., Natraj, N., 1996b. Gomory cuts revisited. Operations Research Letters 19, 1–9.
  * Balas and Martin (1980) Balas, E., Martin, C., 1980. Pivot and complement—a heuristic for 0-1 programming. Management Science 26, 86–96.
  * Balcik and Beamon (2008) Balcik, B., Beamon, B. M., 2008. Facility location in humanitarian relief. International Journal of Logistics 11 (2), 101–121.
  * Baldacci et al. (2004) Baldacci, R., Hadjiconstantinou, E., Mingozzi, A., 2004. An exact algorithm for the capacitated vehicle routing problem based on a two-commodity network flow formulation. Operations Research 52 (5), 723–738.
  * Baldacci et al. (2011) Baldacci, R., Mingozzi, A., Roberti, R., 2011. New route relaxation and pricing strategies for the vehicle routing problem. Operations Research 59 (5), 1269–1283.
  * Ballestín (2007a) Ballestín, F., 2007a. A genetic algorithm for the resource renting problem with minimum and maximum time lags. In: Cotta, C., Hemert, J. (Eds.), Evolutionary Computation in Combinatorial Optimization. Springer, Berlin, Heidelberg, pp. 25–35.
  * Ballestín (2007b) Ballestín, F., 2007b. When it is worthwhile to work with the stochastic RCPSP? Journal of Scheduling 10 (3), 153–166.
  * Ballestín and Leus (2009) Ballestín, F., Leus, R., 2009. Resource-constrained project scheduling for timely project completion with stochastic activity durations. Production and Operations Management 18 (4), 459–474.
  * Balon et al. (2006) Balon, S., Skivée, F., Leduc, G., 2006. How well do traffic engineering objective functions meet TE requirements? In: Proceedings of IFIP Networking 2006, Coimbra. Vol. 3976. Springer LNCS, pp. 75–86.
  * Bandi and Bertsimas (2014) Bandi, C., Bertsimas, D., 2014. Robust option pricing. European Journal of Operational Research 239 (3), 842–853.
  * Bandi and Gupta (2020) Bandi, C., Gupta, D., 2020. Operating room staffing and scheduling. Manufacturing & Service Operations Management 22 (5), 958–974.
  * Banerjee et al. (2022) Banerjee, D., Erera, A. L., Toriello, A., 2022. Fleet sizing and service region partitioning for same-day delivery systems. Transportation Science 56 (5), 1327–1347.
  * Banker et al. (1984) Banker, R. D., Charnes, A., Cooper, W. W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30 (9), 1078–1092.
  * Banker and Morey (1986) Banker, R. D., Morey, R. C., 1986. Efficiency analysis for exogenously fixed inputs and outputs. Operations Research 34 (4), 513–521.
  * Banks et al. (2022) Banks, D., Gallego, V., Naveiro, R., Ríos Insua, D., 2022. Adversarial risk analysis: An overview. Wiley Interdisciplinary Reviews: Computational Statistics 14, 1530.
  * Banks et al. (2004) Banks, J., Carson, J., Nelson, B. L., Nicol, D., 2004. Discrete-Event System Simulation, 4th Edition. Prentice Hall.
  * Barbosa-Póvoa et al. (2018) Barbosa-Póvoa, A. P., da Silva, C., Carvalho, A., 2018. Opportunities and challenges in sustainable supply chain: An operations research perspective. European Journal of Operational Research 268 (2), 399–431.
  * Bardossy and Raghavan (2010) Bardossy, M., Raghavan, S., 2010. Dual-based local search for the connected facility location and related problems. INFORMS Journal on Computing 22, 584–602.
  * Barnhart et al. (2009) Barnhart, C., Farahat, A., Lohatepanont, M., 2009. Airline fleet assignment with enhanced revenue modeling. Operations Research 57 (1), 231–244.
  * Barnhart et al. (2014) Barnhart, C., Fearing, D., Vaze, V., 2014. Modeling passenger travel and delays in the national air transportation system. Operations Research 62 (3), 580–601.
  * Barnhart et al. (2002) Barnhart, C., Kniker, T. S., Lohatepanont, M., 2002. Itinerary-based airline fleet assignment. Transportation Science 36 (2), 199–217.
  * Barnhart and Schneur (1996) Barnhart, C., Schneur, R. R., 1996. Air network design for express shipment service. Operations Research 44 (6), 852–863.
  * Barocas et al. (2019) Barocas, S., Hardt, M., Narayanan, A., 2019. Fairness and Machine Learning: Limitations and Opportunities. fairmlbook.org, http://www.fairmlbook.org.
  * Barrena et al. (2014a) Barrena, E., Canca, D., Coelho, L. C., Laporte, G., 2014a. Exact formulations and algorithm for the train timetabling problem with dynamic demand. Computers & Operations Research 44, 66–74.
  * Barrena et al. (2014b) Barrena, E., Canca, D., Coelho, L. C., Laporte, G., 2014b. Single-line rail rapid transit timetabling under dynamic passenger demand. Transportation Research Part B: Methodological 70, 134–150.
  * Baryannis et al. (2019) Baryannis, G., Validi, S., Dani, S., Antoniou, G., 2019. Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research 57 (7), 2179–2202.
  * Baskett et al. (1975) Baskett, F., Chandy, K. M., Muntz, R. R., Palacios, F. G., 1975. Open, closed, and mixed networks of queues with different classes of customers. Journal of the ACM 22 (2), 248–260.
  * Basole et al. (2022) Basole, R., Bendoly, E., Chandrasekaran, A., Linderman, K., 2022. Visualization in operations management research. INFORMS Journal on Data Science 1 (2), 172–187.
  * Bates and Granger (1969) Bates, J. M., Granger, C. W., 1969. The combination of forecasts. Journal of the Operational Research Society 20 (4), 451–468.
  * Bates et al. (2020) Bates, T., Cobo, C., Mariño, O., Wheeler, S., 2020. Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education 17 (1), 1–12.
  * Baxter et al. (2020) Baxter, A. E., Wilborn Lagerman, H. E., Keskinocak, P., 2020. Quantitative modeling in disaster management: A literature review. IBM Journal of Research and Development 64 (1/2), 3:1–3:13.
  * Bayram and Yaman (2018) Bayram, V., Yaman, H., 2018. Shelter location and evacuation route assignment under uncertainty: A Benders decomposition approach. Transportation Science 52 (2), 416–436.
  * Bazaraa et al. (2005) Bazaraa, M. S., Sherali, H. D., Shetty, C. M., 2005. Nonlinear Programming - Theory and Algorithms, 3rd Edition. Wiley, Hoboken, NJ.
  * Beamon (1999) Beamon, B. M., 1999. Designing the green supply chain. Logistics Information Management 12 (6), 332–342.
  * Beasley (1990) Beasley, J. E., 1990. OR-Library: distributing test problems by electronic mail. Journal of the Operational Research Society 41 (11), 1069–1072.
  * Beer (1966) Beer, S., 1966. Decision and Control. Wiley, Chichester.
  * Beer (1981) Beer, S., 1981. Brain of the firm, 2nd Edition. Wiley, Chichester.
  * Beham (2020) Beham, A., 2020. SimSharp.
URL https://github.com/heal-research/SimSharp

  * Behl and Dutta (2019) Behl, A., Dutta, P., 2019. Humanitarian supply chain management: a thematic literature review and future directions of research. Annals of Operations Research 283 (1), 1001–1044.
  * Bell et al. (1988) Bell, D., Raiffa, H., Tversky, A., 1988. Descriptive, Normative, and Prescriptive Interactions in Decision Making. Cambridge University Press, Cambridge.
  * Bell and Davison (2013) Bell, E., Davison, J., 2013. Visual management studies: Empirical and theoretical approaches. International Journal of Management Reviews 15 (2), 167–184.
  * Bell and O’Keefe (1995) Bell, P. C., O’Keefe, R. M., 1995. An experimental investigation into the efficacy of visual interactive simulation. Management Science 41 (6), 1018–1038.
  * Bell (2012) Bell, S., 2012. DPSIR=A problem structuring method? An exploration from the “Imagine” approach. European Journal of Operational Research 222 (2), 350–360.
  * Bell et al. (1983) Bell, W. J., Dalberto, L. M., Fisher, M. L., Greenfield, A. J., Jaikumar, R., Kedia, P., Mack, R. G., Prutzman, P. J., 1983. Improving the distribution of industrial gases with an on-line computerized routing and scheduling optimizer. Interfaces 13 (6), 4–23.
  * Bellenguez et al. (2023) Bellenguez, O., Brauner, N., Tsoukiàs, A., 2023. Is there an ethical operational research practice? and what this implies for our research? EURO Journal on Decision Processes 11, 100029.
  * Bellman (1953) Bellman, R., 1953. An introduction to the theory of dynamic programming. Tech. rep., RAND Corp Santa Monica CA.
  * Bellman (1957) Bellman, R., 1957. Dynamic Programming. Princeton University Press.
  * Bellmore and Nemhauser (1968) Bellmore, M., Nemhauser, G. L., 1968. The traveling salesman problem: A survey. Operations Research 16 (3), 538–558.
  * Belobaba et al. (2015) Belobaba, P., Odoni, A., Barnhart, C., 2015. The global airline industry, 2nd Edition. John Wiley & Sons.
  * Belobaba (1987a) Belobaba, P. P., 1987a. Air travel demand and airline seat inventory management. Thesis, Massachusetts Institute of Technology.
  * Belobaba (1987b) Belobaba, P. P., 1987b. Survey paper—airline yield management an overview of seat inventory control. Transportation Science 21 (2), 63–73.
  * Belotti et al. (2009) Belotti, P., Berthold, T., Bonami, P., Cafieri, S., Margot, F., Megaw, C., Vigerske, S., Wächter, A., 2009. Couenne.
URL https://projects.coin-or.org/Couenne

  * Belton and Stewart (2002) Belton, V., Stewart, T., 2002. Multiple Criteria Decision Analysis: An Integrated Approach. Springer-Verlag, Berlin.
  * Ben-Ameur et al. (2007) Ben-Ameur, H., Breton, M., Karoui, L., L’Ecuyer, P., 2007. A dynamic programming approach for pricing options embedded in bonds. Journal of Economic Dynamics and Control 31 (7), 2212–2233.
  * Ben-Ameur et al. (2002) Ben-Ameur, H., Breton, M., L’Ecuyer, P., 2002. A dynamic programming procedure for pricing American-style Asian options. Management Science 48 (5), 625–643.
  * Ben-Tal et al. (2009) Ben-Tal, A., El Ghaoui, L., Nemirovski, A., 2009. Robust Optimization. Princeton University Press.
  * Ben-Tal et al. (2005) Ben-Tal, A., Golany, B., Nemirovski, A., Vial, J.-P., 2005. Retailer-Supplier flexible commitments contracts: A robust optimization approach. Manufacturing & Service Operations Management 7 (3), 248–271.
  * Ben-Tal and Nemirovski (2002) Ben-Tal, A., Nemirovski, A., 2002. Robust optimization – methodology and applications. Mathematical Programming 92 (3), 453–480.
  * Benders (1962) Benders, J. F., 1962. Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik 4 (1), 238–252.
  * Benders (2005) Benders, J. F., 2005. Partitioning procedures for solving mixed-variables programming problems. Computational Management Science 2 (1), 3–19.
  * Bendoly and Clark (2016) Bendoly, E., Clark, S., 2016. Visual Analytics for Management: Translational Science and Applications in Practice. Routledge.
  * Bengio et al. (2021) Bengio, Y., Lodi, A., Prouvost, A., 2021. Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research 290 (2), 405–421.
  * Bennell et al. (2018) Bennell, J. A., Cabo, M., Martínez-Sykora, A., 2018. A beam search approach to solve the convex irregular bin packing problem with guillotine cuts. European Journal of Operational Research 270 (1), 89–102.
  * Bennell and Oliveira (2008) Bennell, J. A., Oliveira, J. F., 2008. The geometry of nesting problems: A tutorial. European Journal of Operational Research 184 (2), 397–415.
  * Bennell and Oliveira (2009) Bennell, J. A., Oliveira, J. F., 2009. A tutorial in irregular shape packing problems. Journal of the Operational Research Society 60 (1), S93–S105.
  * Bennis and Nanus (1985) Bennis, W. G., Nanus, B., 1985. Leaders: Strategies for Taking Charge. Harper & Row, New York, NY.
  * Benson and Sağlam (2013) Benson, H., Sağlam, Ü., 2013. Mixed-integer second-order cone programming: a survey. In: Topaloglu, H. (Ed.), Theory Driven by Influential Applications. INFORMS, Catonsville, MD, pp. 13–36.
  * Bergmeir and Benítez (2012) Bergmeir, C., Benítez, J. M., 2012. On the use of cross-validation for time series predictor evaluation. Information Sciences 191, 192–213.
  * Berkey and Wang (1987) Berkey, J. O., Wang, P. Y., 1987. Two-Dimensional finite Bin-Packing algorithms. Journal of the Operational Research Society 38 (5), 423–429.
  * Bernhard (2005) Bernhard, P., 2005. The robust control approach to option pricing and interval models: An overview. In: Breton, M., Ben-Ameur, H. (Eds.), Numerical Methods in Finance. Springer, New York, NY, pp. 91–108.
  * Bertsekas (2012a) Bertsekas, D., 2012a. Dynamic programming and optimal control (Volume I). Athena Scientific, Belmont, MA.
  * Bertsekas (2012b) Bertsekas, D., 2012b. Dynamic programming and optimal control (Volume II: Approximate dynamic programming). Athena Scientific, Belmont, MA.
  * Bertsekas and Tsitsiklis (1996) Bertsekas, D., Tsitsiklis, J. N., 1996. Neuro-Dynamic Programming. Athena Scientific.
  * Bertsekas (2016) Bertsekas, D. P., 2016. Nonlinear programming, 3rd Edition. Athena Scientific, Belmont, MA.
  * Bertsekas et al. (1997) Bertsekas, D. P., Tsitsiklis, J. N., Wu, C., 1997. Rollout algorithms for combinatorial optimization. Journal of Heuristics 3 (3), 245–262.
  * Bertsimas et al. (2011a) Bertsimas, D., Brown, D. B., Caramanis, C., 2011a. Theory and applications of robust optimization. SIAM Review 53 (3), 464–501.
  * Bertsimas and De Boer (2005) Bertsimas, D., De Boer, S., 2005. Simulation-based booking limits for airline revenue management. Operations Research 53 (1), 90–106.
  * Bertsimas and Lo (1998) Bertsimas, D., Lo, A. W., 1998. Optimal control of execution costs. Journal of Financial Markets 1 (1), 1–50.
  * Bertsimas et al. (2011b) Bertsimas, D., Lulli, G., Odoni, A., 2011b. An integer optimization approach to large-scale air traffic flow management. Operations Research 59 (1), 211–227.
  * Bertsimas and Mourtzinou (1997) Bertsimas, D., Mourtzinou, G., 1997. Transient laws of non-stationary queueing systems and their applications. Queueing Systems 25 (1), 115–155.
  * Bertsimas and Nakazato (1995) Bertsimas, D., Nakazato, D., 1995. The distributional Little’s law and its applications. Operations Research 43 (2), 298–310.
  * Bertsimas and Patterson (1998) Bertsimas, D., Patterson, S. S., 1998. The air traffic flow management problem with enroute capacities. Operations Research 46 (3), 406–422.
  * Berwick (2005) Berwick, D. M., 2005. The John Eisenberg lecture: health services research as a citizen in improvement. Health Services Research 40 (2), 317–336.
  * Besiou et al. (2018) Besiou, M., Pedraza-Martinez, A. J., Van Wassenhove, L. N., 2018. OR applied to humanitarian operations. European Journal of Operational Research 269 (2), 397–405.
  * Besiou and Van Wassenhove (2021) Besiou, M., Van Wassenhove, L. N., 2021. System dynamics for humanitarian operations revisited. Journal of Humanitarian Logistics and Supply Chain Management 11 (4), 599–608.
  * Bestuzheva et al. (2021) Bestuzheva, K., Besançon, M., Chen, W.-K., Chmiela, A., Donkiewicz, T., van Doornmalen, J., Eifler, L., Gaul, O., Gamrath, G., Gleixner, A., Gottwald, L., Graczyk, C., Halbig, K., Hoen, A., Hojny, C., van der Hulst, R., Koch, T., Lübbecke, M., Maher, S. J., Matter, F., Mühmer, E., Müller, B., Pfetsch, M. E., Rehfeldt, D., Schlein, S., Schlösser, F., Serrano, F., Shinano, Y., Sofranac, B., Turner, M., Vigerske, S., Wegscheider, F., Wellner, P., Weninger, D., Witzig, J., 2021. The SCIP Optimization Suite 8.0.
URL https://www.scipopt.org/

  * Better and Glover (2011) Better, M., Glover, F., 2011. Simulation optimization in risk management. In: Cochran, J. J., Cox Jr., L. A., Keskinocak, P., Kharoufeh, J. P., Smith, J. C. (Eds.), Wiley Encyclopedia of Operations Research and Management Science. John Wiley & Sons, Hoboken, NJ, pp. 1–9.
  * Beullens et al. (2004) Beullens, P., Van Oudheusden, D., Van Wassenhove, L. N., 2004. Collection and vehicle routing issues in reverse logistics. In: Dekker, R., Fleischmann, M., Inderfurth, K., Van Wassenhove, L. N. (Eds.), Reverse Logistics: Quantitative Models for Closed-Loop Supply Chains. Springer, Berlin, pp. 95–134.
  * Bezanson et al. (2017) Bezanson, J., Edelman, A., Karpinski, S., Shah., V. B., 2017. Julia: A fresh approach to numerical computing. SIAM Review 59, 65–98.
  * Bhatia et al. (2015) Bhatia, R., Hao, F., Kodialam, M., Lakshman, T., 2015. Optimized network traffic engineering using segment routing. 2015 IEEE Conference on Computer Communications (INFOCOM), 657–665.
  * Bi et al. (2021) Bi, X., Zhang, Q., Fan, K., Tang, S., Guan, H., Gao, X., Cui, Y., Ma, Y., Wu, Q., Hao, Y., Ning, N., Liu, C., 2021. Risk culture and COVID-19 protective behaviors: A Cross-Sectional survey of residents in china. Frontiers in Public Health 9, 686705.
  * Bielecki et al. (2017) Bielecki, T. R., Cialenco, I., Pitera, M., 2017. A survey of time consistency of dynamic risk measures and dynamic performance measures in discrete time: LM-measure perspective. Probability Uncertainty and Quantitative Risk 2 (3).
  * Bijvank et al. (2023) Bijvank, M., Huh, W. T., Janakiraman, G., 2023. Lost-sales inventory systems. In: Song, J.-S. (Ed.), Research Handbook on Inventory Management. Edward Elgar Publishing.
  * Billionnet (2013) Billionnet, A., 2013. Mathematical optimization ideas for biodiversity conservation. European Journal of Operational Research 231 (3), 514–534.
  * Bimpikis and Markakis (2016) Bimpikis, K., Markakis, M. G., 2016. Inventory pooling under heavy-tailed demand. Management Science 62 (6), 1800–1813.
  * Binmore et al. (1986) Binmore, K., Rubinstein, A., Wolinsky, A., 1986. The Nash bargaining solution in economic modeling. RAND Journal of Economics 17, 176–188.
  * Birge (1982) Birge, J. R., 1982. The value of the stochastic solution in stochastic linear programs with fixed recourse. Mathematical Programming 24 (1), 314–325.
  * Birge (1985) Birge, J. R., 1985. Decomposition and partitioning methods for multistage stochastic linear programs. Operations Research 33 (5), 989–1007.
  * Birge and Louveaux (2011) Birge, J. R., Louveaux, F., 2011. Introduction to Stochastic Programming. Springer.
  * Bischoff et al. (2018) Bischoff, J., Kaddoura, I., Maciejewski, M., Nagel, K., 2018. Simulation-based optimization of service areas for pooled ride-hailing operators. Procedia Computer Science 130, 816–823.
  * Bixby et al. (1992) Bixby, R., Boyd, E., Indovina, R., 1992. MIPLIB: a test set of mixed integer programming problems. SIAM News 25, 16.
  * Black and Andersen (2012) Black, L. J., Andersen, D. F., 2012. Using visual representations as boundary objects to resolve conflict in collaborative model-building approaches. Systems Research and Behavioral Science 29 (2), 194–208.
  * Blackwell et al. (2008) Blackwell, A. F., Phaal, R., Eppler, M., Crilly, N., 2008. Strategy roadmaps: New forms, new practices. In: Stapleton, G., Howse, J., Lee, J. (Eds.), Diagrammatic Representation and Inference. Springer, Berlin, Heidelberg, pp. 127–140.
  * Blanchet et al. (2022) Blanchet, J., Chen, L., Zhou, X. Y., 2022. Distributionally robust mean-variance portfolio selection with wasserstein distances. Management Science 68 (9), 6382–6410.
  * Bland (1977) Bland, R. G., 1977. New finite pivoting rules for the simplex method. Mathematics of Operations Research 2 (2), 103–107.
  * Bland et al. (1981) Bland, R. G., Goldfarb, D., Todd, M. J., 1981. Feature Article—The ellipsoid method: A survey. Operations Research 29 (6), 1039–1091.
  * Błażewicz et al. (2001) Błażewicz, J., Ecker, K., Pesch, E., Schmidt, G., Weglarz, J., 2001. Scheduling Computer and Manufactoring Processes. Springer, Berlin.
  * Błażewicz et al. (2007) Błażewicz, J., Ecker, K., Pesch, E., Schmidt, G., Weglarz, J., 2007. Handbook on Scheduling: From Theory to Applications. Springer, Berlin.
  * Bley et al. (2009) Bley, A., Fortz, B., Gourdin, E., Holmberg, K., Klopfenstein, O., Pióro, M., Tomaszewski, A., Ümit, H., 2009. Optimization of OSPF routing in IP networks. In: Koster, A., Muñoz, X. (Eds.), Graphs and Algorithms in Communication Networks. Springer, pp. 199–240.
  * Bloomfield and Cox (1972) Bloomfield, P., Cox, D., 1972. A low traffic approximation for queues. Journal of Applied Probability 9 (4), 832–840.
  * Blum and Roli (2003) Blum, C., Roli, A., 2003. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys (CSUR) 35 (3), 268–308.
  * Blum et al. (1998) Blum, L., Cucker, F., Shub, M., Smale, S., 1998. Complexity and Real Computation. Springer, New York, NY.
  * Blumenfeld et al. (1985) Blumenfeld, D. E., Burns, L. D., Diltz, J. D., Daganzo, C. F., 1985. Analyzing trade-offs between transportation, inventory and production costs on freight networks. Transportation Research Part B: Methodological 19 (5), 361–380.
  * Board et al. (2003) Board, J., Sutcliffe, C., Ziemba, W. T., 2003. Applying operations research techniques to financial markets. Interfaces 33 (2), 12–24.
  * Boginski et al. (2015) Boginski, V., Pasiliao, E. L., Shen, S., 2015. Special issue on optimization in military applications. Optimization Letters 9 (8), 1475–1476.
  * Boland et al. (2017) Boland, N., Hewitt, M., Marshall, L., Savelsbergh, M., 2017. The continuous-time service network design problem. Operations Research 65 (5), 1303–1321.
  * Boland et al. (2019) Boland, N., Hewitt, M., Marshall, L., Savelsbergh, M., 2019. The price of discretizing time: a study in service network design. EURO Journal on Transportation and Logistics 8 (2), 195–216.
  * Bommasani et al. (2021) Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N. S., Chen, A. S., Creel, K. A., Davis, J., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L. E., Goel, K., Goodman, N. D., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D. E., Hong, J., Hsu, K., Huang, J., Icard, T. F., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P. W., Krass, M. S., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X. L., Li, X., Ma, T., Malik, A., Manning, C. D., Mirchandani, S. P., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J. C., Nilforoshan, H., Nyarko, J. F., Ogut, G., Orr, L., Papadimitriou, I., Park, J. S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y. H., Ruiz, C., Ryan, J., R’e, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K. P., Tamkin, A., Taori, R., Thomas, A. W., Tramèr, F., Wang, R. E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S. M., Yasunaga, M., You, J., Zaharia, M. A., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P., 2021. On the opportunities and risks of foundation models. arXiv:2108.07258.
  * Bonami et al. (2005) Bonami, P., Biegler, L., Conn, A., Cornuéjols, G., Grossmann, I., Laird, C., Lee, J., Lodi, A., Margot, F., Sawaya, N., Waechter, A., 2005. An Algorithmic Framework for Convex Mixed Integer Nonlinear Programs.
URL https://projects.coin-or.org/Bonmin

  * Bordignon et al. (2013) Bordignon, S., Bunn, D. W., Lisi, F., Nan, F., 2013. Combining day-ahead forecasts for British electricity prices. Energy Economics 35, 88–103.
  * Borůvka (1926) Borůvka, O., 1926. O jistém problému minimálním. Práce Moravsk’e Přírodovědecké Společnosti, 37–58.
  * Bortfeldt and Wäscher (2013) Bortfeldt, A., Wäscher, G., 2013. Constraints in container loading – a state-of-the-art review. European Journal of Operational Research 229 (1), 1–20.
  * Boschetti and Maniezzo (2022) Boschetti, M. A., Maniezzo, V., 2022. Matheuristics: using mathematics for heuristic design. 4OR - A Quarterly Journal of Operations Research 20 (2), 173–208.
  * Bouleimen and Lecocq (2003) Bouleimen, K., Lecocq, H., 2003. A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. European Journal of Operational Research 149 (2), 268–281.
  * Boute et al. (2022) Boute, R., Disney, S. M., Gijsbrechts, J., Mieghem, J. A. V., 2022. Dual sourcing and smoothing under non-stationary demand time series: Re-shoring with SpeedFactories. Management Science 68, 1039–1057.
  * Bowen (1995) Bowen, K. (Ed.), 1995. In at the Deep End: MSc Student Projects in Community Operational Research. Community Operational Research Unit Publications.
  * Box et al. (1976) Box, George, E. P., Jenkins, Gwilym, 1976. Time Series Analysis Forecasting and Control. Holden-Day, San Francisco, CA.
  * Boxma and Cohen (2000) Boxma, O., Cohen, J., 2000. The single server queue: Heavy tails and heavy traffic. In: Park, K., Willinger, W. (Eds.), Self-Similar Network Traffic and Performance Evaluation. Wiley Online Library, pp. 143–169.
  * Boyd et al. (2007) Boyd, A., Geerling, T., Gregory, W. J., Kagan, C., Midgley, G., Murray, P., Walsh, M. P., 2007. Systemic evaluation: a participative, multi-method approach. Journal of the Operational Research Society 58 (10), 1306–1320.
  * Boykov and Kolmogorov (2004) Boykov, Y., Kolmogorov, V., 2004. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (9), 1124–1137.
  * Boysen et al. (2012) Boysen, N., Fliedner, M., Jaehn, F., Pesch, E., 2012. Shunting yard operations: Theoretical aspects and applications. European Journal of Operational Research 220, 1–14.
  * Brailsford and Schmidt (2003) Brailsford, S., Schmidt, B., 2003. Towards incorporating human behaviour in models of health care systems: An approach using discrete event simulation. European Journal of Operational Research 150 (1), 19–31.
  * Brailsford et al. (2004) Brailsford, S. C., Lattimer, V. A., Tarnaras, P., Turnbull, J. C., 2004. Emergency and on-demand health care: modelling a large complex system. Journal of the Operational Research Society 55 (1), 34–42.
  * Bramson (1998) Bramson, M., 1998. State space collapse with application to heavy traffic limits for multiclass queueing networks. Queueing Systems 30 (1), 89–140.
  * Branke et al. (2016) Branke, J., Corrente, S., Greco, S., Słowiński, R., Zielniewicz, P., 2016\. Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. European Journal of Operational Research 250 (3), 884–901.
  * Branke et al. (2008) Branke, J., Deb, K., Miettinen, K., Slowiński, R. (Eds.), 2008. Multiobjective Optimization: Interactive and Evolutionary Approaches. Vol. 5252 of Lecture Notes in Computer Science. Springer-Verlag, Berlin.
  * Brans and Gallo (2007) Brans, J.-P., Gallo, G., 2007. Ethics in OR/MS: past, present and future. Annals of Operations Research 153 (1), 165–178.
  * Brax and Visintin (2017) Brax, S. A., Visintin, F., 2017. Meta-model of servitization: The integrative profiling approach. Industrial Marketing Management 60, 17–32.
  * Breiman (2001) Breiman, L., 2001. Random forests. Machine Learning 45 (1), 5–32.
  * Bresciani and Eppler (2015) Bresciani, S., Eppler, M. J., 2015. The pitfalls of visual representations: A review and classification of common errors made while designing and interpreting visualizations. SAGE Open 5 (4), 2158244015611451.
  * Bresciani and Eppler (2018) Bresciani, S., Eppler, M. J., 2018. The collaborative dimensions of argument maps: A socio-visual approach. Semiotica 2018 (220), 199–216.
  * Breton (2018) Breton, M., 2018. Dynamic games in finance. In: Duan, J.-C., Härdle, W. K., Gentle, J. E. (Eds.), Handbook of Dynamic Game Theory. Springer, pp. 827–863.
  * Breton and Marzouk (2018) Breton, M., Marzouk, O., 2018. Evaluation of counterparty risk for derivatives with early-exercise features. Journal of Economic Dynamics and Control 88, 1–20.
  * Brettel et al. (2014) Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M., 2014. How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. International Journal of Mechanical, Industrial Science and Engineering 8 (1), 37–44.
  * Brezavšček et al. (2017) Brezavšček, A., Bach, M. P., Baggia, A., 2017. Markov analysis of students’ performance and academic progress in higher education. Organizacija 50 (2), 83–95.
  * Briskorn et al. (2010) Briskorn, D., Drexl, A., Spieksma, F. C. R., 2010. Round robin tournaments and three index assignments. 4OR - A Quarterly Journal of Operations Research 8 (4), 365–374.
  * Broadie (2012) Broadie, M., 2012. Assessing golfer performance on the PGA TOUR. Interfaces 42 (2), 146–165.
  * Broadie et al. (2007) Broadie, M., Chernov, M., Sundaresan, S., 2007. Optimal debt and equity values in the presence of Chapter 7 and Chapter 11. The Journal of Finance 62 (3), 1341–1377.
  * Brooks-Pollock et al. (2021) Brooks-Pollock, E., Danon, L., Jombart, T., Pellis, L., 2021. Modelling that shaped the early COVID-19 pandemic response in the UK. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 376 (1829), 20210001.
  * Brotcorne et al. (2003) Brotcorne, L., Laporte, G., Semet, F., 2003. Ambulance location and relocation models. European Journal of Operational Research 147 (3), 451–463.
  * Brouer et al. (2014) Brouer, B. D., Alvarez, J. F., Plum, C. E. M., Pisinger, D., Sigurd, M. M., 2014\. A base integer programming model and benchmark suite for Liner-Shipping network design. Transportation Science 48 (2), 281–312.
  * Brown et al. (2005) Brown, G., Carlyle, M., Diehl, D., Kline, J., Wood, K., 2005. A two-sided optimization for theater ballistic missile defense. Operations Research 53 (5), 745–763.
  * Brown et al. (2004) Brown, G. G., Dell, R. F., Newman, A. M., 2004. Optimizing military capital planning. Interfaces 34 (6), 415–425.
  * Brown (1956) Brown, R. G., 1956. Exponential smoothing for predicting demand. Little, Cambridge, MA.
  * Browne et al. (1984) Browne, J., Dubois, D., Sethi, S., Rathmill, K., Stecke, K., 1984. Classification of flexible manufacturing systems. The FMS Magazine 2 (2), 114–117.
  * Brucker et al. (1998) Brucker, P., Knust, S., Schoo, A., Thiele, O., 1998. A branch and bound algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research 107 (2), 272–288.
  * Brumelle and McGill (1993) Brumelle, S. L., McGill, J. I., 1993. Airline seat allocation with multiple nested fare classes. Operations Research 41 (1), 127–137.
  * Brumelle et al. (1990) Brumelle, S. L., McGill, J. I., Oum, T. H., Sawaki, K., Tretheway, M., 1990. Allocation of airline seats between stochastically dependent demands. Transportation Science 24 (3), 183–192.
  * Bryant (1978) Bryant, J. W., 1978. Modelling for natural resource utilization analysis. Journal of the Operational Research Society 29 (7), 667–676.
  * Buhaug (2002) Buhaug, H., 2002. Long waiting lists in hospitals. BMJ 324 (7332), 252–253.
  * Bulut et al. (2019) Bulut, A., Xu, Y., Ralphs, T., Vigerske, S., 2019. Discrete Conic Optimization Solver.
URL https://projects.coin-or.org/Disco

  * Buraimo et al. (2020) Buraimo, B., Forrest, D., McHale, I. G., Tena, J. D., 2020. Unscripted drama: Soccer audience response to suspense, surprise, and shock. Economic Inquiry 58 (2), 881–896.
  * Burdett and Kozan (2009) Burdett, E. L., Kozan, E., 2009. Techniques for inserting additional trains into existing timetables. Transportation Research Part B: Methodological 43 (8-9), 821–836.
  * Burger et al. (2019) Burger, K., White, L., Yearworth, M., 2019. Developing a smart operational research with hybrid practice theories. European Journal of Operational Research 277 (3), 1137–1150.
  * Burk and Parnell (2011) Burk, R. C., Parnell, G. S., 2011. Portfolio decision analysis: Lessons from military applications. In: Salo, A., Keisler, J., Morton, A. (Eds.), Portfolio Decision Analysis: Improved Methods for Resource Allocation. Springer, New York, NY, pp. 333–357.
  * Burkard et al. (2012) Burkard, R., Dell’Amico, M., Martello, S., 2012. Assignment problems, revised reprint. SIAM, Philadelphia.
  * Burkard and Derigs (1980) Burkard, R., Derigs, U., 1980. Assignment and Matching Problems: Solution Methods with FORTRAN Programs. Springer-Verlag, Berlin, Heidelberg.
  * Burke and Bykov (2008) Burke, E. K., Bykov, Y., 2008. A late acceptance strategy in hill-climbing for exam timetabling problems. In: Proceedings of the International Conference of the Practice and Theory of Automated Timetabling (PATAT 2008). pp. 1–7.
  * Burke and Curtois (2014) Burke, E. K., Curtois, T., 2014. New approaches to nurse rostering benchmark instances. European Journal of Operational Research 237, 71–81.
  * Burke et al. (2004a) Burke, E. K., De Causmaecker, P., Vanden Berghe, G., Van Landeghem, H., 2004a. The state of the art of nurse rostering. Journal of Scheduling 7 (6), 441–499.
  * Burke et al. (2004b) Burke, E. K., Kendall, G., Whitwell, G., 2004b. A new placement heuristic for the orthogonal Stock-Cutting problem. Operations Research 52 (4), 655–671.
  * Burke et al. (2006) Burke, E. K., MacCarthy, B. L., Petrovic, S., Qu, R., 2006. Multiple-retrieval case-based reasoning for course timetabling problems. Journal of the Operational Research Society 57 (2), 148–162.
  * Burke and Petrovic (2002) Burke, E. K., Petrovic, S., 2002. Recent research directions in automated timetabling. European Journal of Operational Research 140 (2), 266–280.
  * Burman and Smith (1983) Burman, D. Y., Smith, D. R., 1983. A light-traffic theorem for multi-server queues. Mathematics of Operations Research 8 (1), 15–25.
  * Burney et al. (2013) Burney, N. A., Johnes, J., Al-Enezi, M., Al-Musallam, M., 2013. The efficiency of public schools: the case of Kuwait. Education Economics 21 (4), 360–379.
  * Büsing et al. (2019) Büsing, C., Kadatz, D., Cleophas, C., 2019. Capacity uncertainty in airline revenue management: Models, algorithms, and computations. Transportation Science 53 (2), 383–400.
  * Bussieck et al. (2004) Bussieck, M. R., Lindner, T., Lubbecke, M. E., 2004. A fast algorithm for near cost optimal line plans. Mathematical Methods of Operations Research 59, 205–220.
  * Bussieck et al. (1997) Bussieck, M. R., Winter, T., Zimmerman, U. T., 1997. Discrete optimization in public rail trainsport. Mathematical Programming 79, 415– 444.
  * Butcher et al. (2021) Butcher, B., Huang, V. S., Robinson, C., Reffin, J., Sgaier, S. K., Charles, G., Quadrianto, N., 2021. Causal datasheet for datasets: An evaluation guide for Real-World data analysis and data collection design using bayesian networks. Frontiers in Artificial Intelligence 4, 612551.
  * Büyüktahtakın and Haight (2018) Büyüktahtakın, İ. E., Haight, R. G., 2018. A review of operations research models in invasive species management: state of the art, challenges, and future directions. Annals of Operations Research 271 (2), 357–403.
  * Bykov and Petrovic (2016) Bykov, Y., Petrovic, S., 2016. A step counting hill climbing algorithm applied to university examination timetabling. Journal of Scheduling 19, 479–492.
  * Bynum et al. (2021) Bynum, M. L., Hackebeil, G. A., Hart, W. E., Laird, C. D., Nicholson, B. L., Siirola, J. D., Watson, J.-P., Woodruff, D. L., 2021. Pyomo–optimization modeling in Python, 3rd Edition. Vol. 67. Springer Science & Business Media.
URL http://www.pyomo.org/

  * Bö$\eth$varsdóttir et al. (2021) Bö$\eth$varsdóttir, E. B., Smet, P., Vanden Berghe, G., Stidsen, T. J., 2021\. Achieving compromise solutions in nurse rostering by using automatically estimated acceptance thresholds. European Journal of Operational Research 292 (3), 980–995.
  * Büyüktahtakin et al. (2014) Büyüktahtakin, I. E., Feng, Z., Szidarovszky, F., 2014. A multi-objective optimization approach for invasive species control. Journal of the Operational Research Society 65 (11), 1625–1635.
  * Cabrera et al. (2023) Cabrera, D., Cabrera, L., Midgley, G., 2023. The four waves of systems thinking. In: Cabrera, D., Cabrera, L., Midgley, G. (Eds.), Routledge Handbook of Systems Thinking. Routledge, London.
  * Cabrera et al. (2015) Cabrera, D., Cabrera, L., Powers, E., 2015. A unifying theory of systems thinking with psychosocial applications. Systems Research and Behavioral Science 32 (5), 534–545.
  * Cacchiani et al. (2008a) Cacchiani, V., Caprara, A., Galli, L., Kroon, L., Maróti, G., 2008a. Recoverable Robustness for Railway Rolling Stock Planning. In: Fischetti, M., Widmayer, P. (Eds.), 8th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS’08). Vol. 9 of OpenAccess Series in Informatics (OASIcs). Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, pp. 9–10.
  * Cacchiani et al. (2012) Cacchiani, V., Caprara, A., Galli, L., Kroon, L., Maróti, G., Toth, P., 2012\. Railway rolling stock planning: Robustness against large disruptions. Transportation Science 46 (2), 217–232.
  * Cacchiani et al. (2008b) Cacchiani, V., Caprara, A., Toth, P., 2008b. A column generation approach to train timetabling on a corridor. 4OR - A Quarterly Journal of Operations Research 6 (2), 125–142.
  * Cacchiani et al. (2010) Cacchiani, V., Caprara, A., Toth, P., 2010. Scheduling extra freight trains on railway networks. Transportation Research Part B: Methodological 44 (2), 215–231.
  * Cacchiani et al. (2022a) Cacchiani, V., Iori, M., Locatelli, A., Martello, S., 2022a. Knapsack problems – an overview of recent advances. Part I: Single knapsack problems. Computers & Operations Research 143, 105692.
  * Cacchiani et al. (2022b) Cacchiani, V., Iori, M., Locatelli, A., Martello, S., 2022b. Knapsack problems – an overview of recent advances. Part II: Multiple, multidimensional, and quadratic knapsack problems. Computers & Operations Research 143, 105693.
  * Cachon and Lariviere (2005) Cachon, G. P., Lariviere, M. A., 2005. Supply chain coordination with revenue-sharing contracts: Strengths and limitations. Management Science 51 (1), 30–44.
  * Cadarso and Vaze (2022) Cadarso, L., Vaze, V., 2022. Passenger-centric integrated airline schedule and aircraft recovery. Transportation Science 56 (6), 1410–1431.
  * Cadarso et al. (2017) Cadarso, L., Vaze, V., Barnhart, C., Marín, Á., 2017. Integrated airline scheduling: Considering competition effects and the entry of the high speed rail. Transportation Science 51 (1), 132–154.
  * Çalık et al. (2019) Çalık, H., Labbé, M., Yaman, H., 2019. p-center problems. In: Laporte, G., Nickel, S., Saldanha da Gama, F. (Eds.), Location Science. Springer, pp. 51–65.
  * Callon (1981) Callon, M., 1981. Struggles and negotiations to define what is problematic and what is not. In: Knorr, K. D., Krohn, R., Whitley, R. (Eds.), The Social Process of Scientific Investigation. Springer Netherlands, Dordrecht, pp. 197–219.
  * Calmon and Graves (2017) Calmon, A. P., Graves, S. C., 2017. Inventory management in a consumer electronics Closed-Loop supply chain. Manufacturing & Service Operations Management 19 (4), 568–585.
  * Calmon et al. (2021) Calmon, A. P., Graves, S. C., Lemmens, S., 2021. Warranty matching in a consumer electronics Closed-Loop supply chain. Manufacturing & Service Operations Management 23 (5), 1314–1331.
  * Camacho and Bordons (2013) Camacho, E. F., Bordons, C., 2013. Model Predictive Control. Springer, London.
  * Campbell and Savelsbergh (2006) Campbell, A. M., Savelsbergh, M., 2006. Incentive schemes for attended home delivery services. Transportation Science 40 (3), 327–341.
  * Campbell and Savelsbergh (2005) Campbell, A. M., Savelsbergh, M. W., 2005. Decision support for consumer direct grocery initiatives. Transportation Science 39 (3), 313–327.
  * Campbell and Sayer (2003) Campbell, B. M., Sayer, J. A., 2003. Integrated natural resources management: linking productivity, the environment and development. CABI Publishing, Cambridge, MA.
  * Canca et al. (2018) Canca, D., Andrade-Pineda, J. L., De-los Santos, A., Calle, M., 2018. The railway rapid transit frequency setting problem with speed-dependent operation costs. Transportation Research Part B: Methodological 117 (A), 494–519.
  * Canca et al. (2014) Canca, D., Barrena, E., Algaba, E., Zarzo, A., 2014. Design and analysis of demand‐adapted railway timetables. Journal of Advanced Transportation 48 (2), 119–137.
  * Canca et al. (2016) Canca, D., Barrena, E., De-Los-Santos, A., Andrade-Pineda, J. L., 2016. Setting lines frequency and capacity in dense railway rapid transit networks with simultaneous passenger assignment. Transportation Research Part B: Methodological 93(A), 251–267.
  * Canca et al. (2017) Canca, D., De-Los-Santos, A., Laporte, G., Mesa, J. A., 2017. An adaptive neighborhood search metaheuristic for the integrated railway rapid transit network design and line planning problem. Computers & Operations Research 78, 1–14.
  * Canca et al. (2019) Canca, D., De-Los-Santos, A., Laporte, G., Mesa, J. A., 2019. Integrated railway rapid transit network design and line-planning problem with maximum profit. Transportation Research Part E: Logistics and Transportation Review 127, 1–30.
  * Canca and Zarzo (2017) Canca, D., Zarzo, A., 2017. Design of energy-Efficient timetables in two-way railway rapid transit lines. Transportation Research Part B: Methodological 102, 142–161.
  * Cao et al. (2022) Cao, A., Lan, J., Xie, X., Chen, H., Zhang, X., Zhang, H., Wu, Y., 2022. Team-Builder: Toward more effective lineup selection in soccer. IEEE Transactions on Visualization and Computer Graphics, DOI: 10.1109/TVCG.2022.3207147.
  * Cappart et al. (2021) Cappart, Q., Chételat, D., Khalil, E. B., Lodi, A., Morris, C., Veličković, P., 8 2021. Combinatorial optimization and reasoning with graph neural networks. In: Zhou, Z.-H. (Ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. International Joint Conferences on Artificial Intelligence Organization, pp. 4348–4355, survey Track.
  * Caprara et al. (2014) Caprara, A., Carvalho, M., Lodi, A., Woeginger, G. J., 2014. A study on the computational complexity of the bilevel knapsack problem. SIAM Journal on Optimization 24 (2), 823–838.
  * Caprara et al. (2002) Caprara, A., Fischetti, M., Toth, P., 2002. Modeling and solving the train timetabling problem. Operations Research 50 (5), 851–861.
  * Caprara et al. (2007) Caprara, A., Kroon, L., Monaci, M., Peeters, M., Toth, P., 2007. Passenger railway optimization. In: Barnhart, C., Laporte, G. (Eds.), Handbooks in Operations Research and Management Science. Vol. 14. Transportation. Elsevier, pp. 129–187.
  * Caprara et al. (2003) Caprara, A., Monaci, M., Toth, P., 2003. Models and algorithms for a staff scheduling problem. Mathematical Programming 98 (1), 445–476.
  * Caprara et al. (1998) Caprara, A., Toth, P., Vigo, D., Fischetti, M., 1998. Modeling and solving the crew rostering problem. Operations Research 46 (6), 820–830.
  * Cardaliaguet and Lehalle (2018) Cardaliaguet, P., Lehalle, C.-A., 2018. Mean field game of controls and an application to trade crowding. Mathematics and Financial Economics 12 (3), 335–363.
  * Cardoen et al. (2010) Cardoen, B., Demeulemeester, E., Beliën, J., 2010. Operating room planning and scheduling: A literature review. European Journal of Operational Research 201 (3), 921–932.
  * Caro and Gallien (2012) Caro, F., Gallien, J., 2012. Clearance pricing optimization for a fast-fashion retailer. Operations Research 60 (6), 1404–1422.
  * Carraro and Haurie (1996) Carraro, C., Haurie, A., 1996. Operations Research and Environmental Management. Kluwer Academic, Dordrecht/Boston/London.
  * Carter et al. (1996) Carter, M. W., Laporte, G., Lee, S. Y., 1996. Examination timetabling: Algorithmic strategies and applications. Journal of the Operational Research Society 74, 373–383.
  * Cassady (1967) Cassady, R., 1967. Auctions and Auctioneering. University of California Press, Berkeley, CA.
  * Castelnovo et al. (2022) Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I. G., Cosentini, A. C., 2022. A clarification of the nuances in the fairness metrics landscape. Scientific Reports 12, article 4209.
  * Castillo-Salazar et al. (2016) Castillo-Salazar, A. J., Landa-Silva, D., Qu, R., 2016. Workforce scheduling and routing problems: literature survey and computational study. Annals of Operations Research 239, 39–67.
  * Cataldo et al. (2017) Cataldo, A., Ferrer, J.-C., Miranda, J., Rey, P. A., Sauré, A., 2017. An integer programming approach to curriculum-based examination timetabling. Annals of Operations Research 258 (2), 369–393.
  * Caulkins et al. (2008) Caulkins, J. P., Eelman, E., Ratnatunga, M., Schaarsmith, D., 2008. Operations Research and public policy for Africa: harnessing the revolution in management science instruction. International Transactions in Operational Research 15, 151–171.
  * Cavaliere et al. (2022) Cavaliere, F., Bendotti, E., Fischetti, M., 2022. An integrated local-search/set-partitioning refinement heuristic for the capacitated vehicle routing problem. Mathematical Programming Computation 14, 749–779.
  * Cavallo et al. (2019) Cavallo, B., Ishizaka, A., Olivieri, M. G., Squillante, M., 2019. Comparing inconsistency of pairwise comparison matrices depending on entries. Journal of the Operational Research Society 70 (5), 842–850.
  * Ceder and Wilson (1986) Ceder, A., Wilson, N. H., 1986. Bus network design. Transportation Research Part B: Methodological 20 (4), 331–344.
  * Cegan et al. (2017) Cegan, J. C., Filion, A. M., Keisler, J. M., Linkov, I., 2017. Trends and applications of multi-criteria decision analysis in environmental sciences: literature review. Environment Systems and Decisions 37 (2), 123–133.
  * Cela (2013) Cela, E., 2013. The quadratic assignment problem: theory and algorithms. Springer Science & Business Media, Berlin, Heidelberg.
  * Çelik (2016) Çelik, M., 2016. Network restoration and recovery in humanitarian operations: Framework, literature review, and research directions. Surveys in Operations Research and Management Science 21 (2), 47–61.
  * Çelik et al. (2012) Çelik, M., Ergun, Ö., Johnson, B., Keskinocak, P., Lorca, Á., Pekgün, P., Swann, J., 2012. Humanitarian logistics. In: Mirchandani, P. B. (Ed.), New Directions in Informatics, Optimization, Logistics, and Production. INFORMS TutORials in Operations Research. INFORMS, pp. 18–49.
  * Celikbilek and Süer (2015) Celikbilek, C., Süer, G. A., 2015. Cellular design-based optimisation for manufacturing scheduling and transportation mode decisions. Asian Journal of Management Science and Applications 2 (2), 107–129.
  * Cervone et al. (2016) Cervone, D., D’Amour, A., Bornn, L., Goldsberry, K., 2016. A multiresolution stochastic process model for predicting basketball possession outcomes. Journal of the American Statistical Association 111 (514), 585–599.
  * Ceschia et al. (2019) Ceschia, S., Dang, N., De Causmaecker, P., Haspeslagh, S., Schaerf, A., 2019. The second international nurse rostering competition. Annals of Operations Research 274 (1), 171–186.
  * Ceschia et al. (2022) Ceschia, S., Di Gaspero, L., Schaerf, A., 2022. Educational timetabling: Problems, benchmarks, and state-of-the-art results. European Journal of Operational Research, DOI: 10.1016/j.ejor.2022.07.011.
  * Chae et al. (2016) Chae, Y., Horesh, R., Hwang, Y., Lee, Y., 2016. Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy and Buildings 111, 184–194.
  * Chambers et al. (1996) Chambers, R. G., Färe, R., Grosskopf, S., 1996. Productivity growth in APEC countries. Pacific Economic Review 1 (3), 181–190.
  * Chang et al. (2007) Chang, H. S., Hu, J., Fu, M. C., Marcus, S. I., 2007. Simulation-based algorithms for Markov decision processes. Springer.
  * Chang et al. (2022) Chang, Y., Keblis, M. F., Li, R., Iakovou, E., White, C. C., 2022. Misinformation and disinformation in modern warfare. Operations Research 70 (3), 1577–1597.
  * Chao and Wilson (2002) Chao, H.-P., Wilson, R., 2002. Multi-dimensional procurement auctions for power reserves: Robust incentive-compatible scoring and settlement rules. Journal of Regulatory Economics 22 (2), 161–183.
  * Chao et al. (2023) Chao, X., Chen, B., Zhang, H., 2023. Online learning in inventory and pricing optimization. In: Song, J.-S. (Ed.), Research Handbook on Inventory Management. Edward Elgar Publishing.
  * Charnes and Cooper (1962) Charnes, A., Cooper, W. W., 1962. Programming with linear fractional functionals. Naval Research Logistics Quarterly 9, 181–186.
  * Charnes et al. (1978) Charnes, A., Cooper, W. W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6), 429–444.
  * Charnes et al. (1958) Charnes, A., Cooper, W. W., Symonds, G. H., 1958. Cost horizons and certainty equivalents: An approach to stochastic programming of heating oil. Management Science 4 (3), 235–263.
  * Chazelle (1983) Chazelle, 1983. The Bottomn-Left Bin-Packing heuristic: An efficient implementation. IEEE Transactions on Computers C-32 (8), 697–707.
  * Checkland (1981) Checkland, P., 1981. Systems Thinking, Systems Practice. Wiley, Chichester.
  * Checkland (1983) Checkland, P., 1983. O.R. and the systems movement: Mappings and conflicts. Journal of the Operational Research Society 34 (8), 661–675.
  * Checkland (1985) Checkland, P., 1985. From optimizing to learning: A development of systems thinking for the 1990s. Journal of the Operational Research Society 36 (9), 757–767.
  * Checkland (1989) Checkland, P., 1989. Soft systems methodology. In: Rosenhead, J. (Ed.), Rational analysis for a problematic world: Problem structuring methods for complexity, uncertainty, and conflict. Wiley, Chichester, pp. 71–100.
  * Checkland (2006) Checkland, P., 2006. Reply to Eden and Ackermann: Any future for problem structuring methods? Journal of the Operational Research Society 57 (7), 769–771.
  * Checkland and Poulter (2006) Checkland, P., Poulter, J., 2006. Learning For Action: A Short Definitive Account of Soft Systems Methodology, and Its Use for Practitioners, Teachers and Students. Wiley.
  * Checkland and Scholes (1990) Checkland, P., Scholes, J., 1990. Soft Systems Methodology in Action. Wiley, Chichester.