We have fabricated and successfully tested , for the first time , a prototype chip of a Race Logic computing paradigm , which makes positive use of race conditions for accelerating a broad class of optimization problems , such as ones solved by dynamic programming algorithms . In Race Logic , information is encoded in signal propagation delay , rather than conventional logic levels , and the result of the computation is observed from relative timing differences between injected signals , i.e. the outcome of races . This paper proposes a generic cross-layer optimization framework for cooperative video summary transmission , which jointly considers the source coding , relay processing parameters , power allocation , and error concealment strategy to achieve the best video quality . The problem is solved by using Lagrangian relaxation and dynamic programming ( DP ) . Experimental results show that the proposed scheme significantly outperforms the direct transmission scheme , the multipath scheme , and the decode-and-forward transmission scheme by up to 25 % . Reconfigurable systolic arrays can be adapted to efficiently resolve a wide spectrum of computational problems ; parallelism is naturally explored in systolic arrays and reconfigurability allows for redefinition of the interconnections and operations even during run time ( dynamically ) . We present a reconfigurable systolic architecture that can be applied for the efficient treatment of several dynamic programming methods for resolving well-known problems , such as global and local sequence alignment , approximate string matching and longest common subsequence . The dynamicity of the reconfigurability was found to be useful for practical applications in the construction of sequence alignments . Pure dynamic-programming-based systems are often used for PHMM searches . However , these dynamic-programming- based systems are very time consuming for a large database . For instance , it may take approximately 15 minutes to search a short model of length 12 in the GenBank protein sequence database . In this paper , we review the main causes of editing errors in literature and propose an editing model relying on a minimization of such errors . We make a plausible semi-Markov assumption , resulting in a dynamic programming solution which is computationally efficient . We also show that our method can generate movies with different editing rhythms and validate the results through a user study . Measurement of software trustworthiness is an important research field in the software engineering , which is very useful for analyzing the software quality . In this paper , we propose a mathematical programming approach to allocate the trustworthy degree to each sub-attribute of some software attribute appropriately and then to make the trustworthy degree of this attribute maximize under some constraint conditions . Some sufficient or necessary conditions for analyzing this mathematical programming problem are investigated . Assigning the airport gate with high efficiency is a key task in the airport ground operation . A mathematical programming model was established whose objective function was minimizing the overall variance of slack time(OVST ) to enable the slack time to be uniformly distributed . According to the intrinsic characteristics of the objective function itself , the model was solved by a heuristic algorithm that was designed through rigorous theoretical analysis . We propose a scenario-based solution approach that can deal with a wide range of uncertainty types . The approach is based on an MILP to find a plan with minimum expected costs over all relevant scenarios . To solve this MILP , we propose an exact branch-and-price algorithm . The performance of the ICP approach is compared with other alternative approaches , i.e. , nonlinear programming approaches , a mixed-integer nonlinear programming ( MINLP ) approach , and a mixed-integer linear programming ( MILP ) approach . In addition , this paper formulates the real-time train scheduling problem with stop-skipping and shows how to solve it using an MINLP approach and an MILP approach . The ICP approach is shown , via a case study , to provide a better tradeoff between performance and computational complexity for the real-time train scheduling problem . Therefore , new suitable exploration methods for the 3D thermal-aware floorplaning problem need to be developed . In this paper we analyze the benefits of two different exploration techniques for the floorplanning problem : Multi-Objective Genetic Algorithm ( MOGA ) and a Mixed Integer Linear Program ( MILP ) . We present a novel algorithm that uses MILP to minimize average temperature in the 3D chip , whereas uses MOGA to insert TSVs , connecting the layers while the total wire length is minimized . An effective cut-generating scheme based on temporal analysis in project scheduling is devised for resolving resource conflicts . The computational study shows that our hybrid MILP/CP algorithm is both effective and efficient compared to the pure MILP or CP method alone . An application example is considered in which the least-cost schedule has to be derived for processing a set of orders with release and due dates using a set of dissimilar parallel machines . It is shown that this problem can be modeled as an MILP , a CP , a combined MILP-CP OPL model ( Van Hentenryck 1999 ) , and a hybrid MILP/CP model . The computational performance of these models for several sets shows that the hybrid MILP/CP model can achieve two to three orders of magnitude reduction in CPU time . Finally , these moves have to be sequenced for each crane for processing . In this paper , an integrated MILP model is proposed , which aims to solve the TYSP as a single optimization problem . The proposed formulation also enables us to define more robust and complex objective functions that include key characteristics from each of the above-mentioned subproblems . A refinement procedure is defined that allows to further improve communications by also minimizing network delays . We report a computational analysis of the integrated MILP model and an evaluation of the impact of a number of parameters on the trade-off between computational load and quality of the output . Results show that the model is computationally affordable for reasonably sized scenarios , and can effectively balance different performance trade-offs . Finally , the performance of the priority rule based approach and the MILP model are analysed for different scenarios with respect to total lateness of the AGVs . The main focus of the numerical investigation is on evaluating the priority rule based approach for single and dual-load vehicles as well as comparing its performance against the MILP modelling approach . Abstract The production scheduling problem of a real-life food industry is addressed in this work . An efficient MILP-based solution strategy is developed to optimize weekly schedules for a Spanish canned fish production plant . The multi-stage , multi-product facility under study consists of both continuous and batch operations resulting in an extremely complex scheduling problem . We also compare our results with a recent RCPSP-specific exact method . We show that , in terms of exact solving , no MILP formulation class dominates the other ones and that a state-of-the art specialized ( non-MILP ) method for the RCPSP is even outperformed by MILP on a set of hard instances . Furthermore , on another set of hard ' ' highly cumulative ' ' RCPSP instances with a high processing time range , our On/Off formulation outperforms all the others MILP formulations and obtains results close to the ones of the specialized method . In doing so , we look for a general-purpose approach applicable to any MIBLP under mild conditions , rather than ad-hoc methods for specific cases . Our approach concentrates on minimal additions required to convert an effective branch-and-cut MILP exact code into a valid MIBLP solver , thus inheriting the wide arsenal of MILP tools cuts , branching rules , heuristics available in modern solvers . Abstract This paper describes a model predictive control strategy to find the optimal decision variables to maximize profit in supply chains with multiproduct , multiechelon distribution networks with multiproduct batch plants . The key features of this paper are : ( 1 ) a discrete time MILP dynamic model that considers the flow of material and information within the system ; ( 2 ) a general dynamic optimization framework that simultaneously considers all the elements of the supply chain and their interactions ; and ( 3 ) , a rolling horizon approach to update the decision variables whenever changes affecting the supply chain arise . The paper compares the behavior of a supply chain under centralized and decentralized management approaches , and shows that the former yields better results , with profit increases of up to 15 % as shown in an example problem . In the bi-level programming models , the number of parking permits assigned or held by each OD pair is an implicit function with respect to parking fee . A global optimum oriented solution algorithm and simulated annealing algorithm are used to solve the MILP and bi-level programming models respectively . Numerical examples are carried out to show that the hybrid schemes with parking pricing and parking permits are more effective than both pure parking pricing scheme and pure parking permits management scheme . To reduce the calculation complexity of these models , they are transformed into their equivalent fuzzy chance constrained mixed-integer linear programming ( CFCC-MILP ) models . After that these CFCC-MILP models are solved through their crisp equivalent algorithms based on the theory presented by Liu & Liu [ 2002 ] for the first time in refinery scheduling area . Finally , a case study which has 265 continuous variables , 68 binary variables and 318 constraints is effectively solved in LINGO 8.0 with the proposed approaches . We present mixed integer programming approaches for optimally solving a combinatorial optimization problem arising in network design with additional quality of service constraints . The rooted delay- and delay-variation-constrained Steiner tree problem asks for a cost-minimal Steiner tree satisfying delay-constraints from source to terminals and a maximal variation-bound between particular terminal path-delays . This paper presents the state-of-the-art on application of optimisation techniques in groundwater quality and quantity management . In order to solve optimisation-based groundwater management models , researchers have used various mathematical programming techniques such as linear programming ( LP ) , nonlinear programming ( NLP ) , mixed-integer programming ( MIP ) , optimal control theory-based mathematical programming , differential dynamic programming ( DDP ) , stochastic programming ( SP ) , combinatorial optimisation ( CO ) , and multiple objective programming for multipurpose management . Studies reported in the literature on the application of these methods are reviewed in this paper . The problem involves a heterogeneous fleet of vehicles of different capacities , mandatory visits to a subset of vertices , time windows , rest requirements associated with maximum driving and working times , and partial collection . A mixed integer programming formulation of the problem is described , along with three matheuristics based on Tabu Search and Large Neighbourhood Search . Computational results on instances derived from the case study are presented , and insights are given from a practical implementation . Therefore , in this study two options for each non-hub node are given : ( i ) either it could be visited by a local tour or ( ii ) it could be directly connected to a hub without an economy of scale . We develop a mixed integer programming formulation and strengthen it with valid inequalities . We also develop three different Benders formulations as exact solution methods . For this problem , we develop a 2-flip neighborhood local search algorithm with a heuristic size reduction algorithm , in which a new evaluation scheme of variables is introduced taking account of GUB constraints . According to computational comparison with the latest version of a mixed integer programming solver , our algorithm performs quite effectively for various types of instances , especially for very large-scale instances . Furthermore , we argue that an important step in this approach is finding dual alternative optimal solutions to the Benders subproblems and describe an approach to obtain a diverse set of these . Numerical tests show that the described methodology significantly outperforms a commercial Mixed Integer Programming solver on instances with 1241 different shift types and remains competitive for larger cases with 2145 shift types . On all classes of problems the heuristic is able to quickly find good solutions . This issue has not yet been discussed in the literature . First , a mixed integer programming mathematical model is presented . Then , in order to solve the problem , a new algorithm is proposed which is a new combination of genetic and Simulated Annealing Algorithms . A scientific and reasonable steelmaking - continuous casting production scheduling is the key to improve the production efficiency of the steelmaking - continuous casting production system . In this paper , with the shortest total charge waiting time and the castings that are processed on time as the optimization goal , with the charges that are casted continuous within same casting and machine capacity constraints and so on as the constraints to established a mixed integer programming mathematical model . The augmented Lagrangian relaxation algorithm is used to solved the scheduling problem . Our main goal is to integrate offshore platforms from the chemical and petroleum engineering literature , and capacity investment from operations management literature . We use a mixed integer programming solution approach and set a basic model to analyse the value of operational flexibility . Our main contribution is to provide an operational flexibility option to the problem of oil drilling . The IBM differs from traditional batching formulations by assigning orders to indexed batches , whereby each batch corresponds to a position in the batch release sequence . We develop a mixed integer programming solution for exact control , and demonstrate a simulated annealing procedure to solve large practical problems . Our results indicate that the proposed approach achieves a 5–15 % reduction in the total retrieval time primarily by reducing picker blocking . The approximation generalizes the widely known model for approximating single variable functions , and significantly expands the set of nonlinear problems that can be efficiently solved by reducing them to Mixed Integer Programming Problems . By our development , any nonlinear programming problem , including non-convex ones , with an objective function ( and/or constraints ) that can be expressed as sums of component nonlinear functions of no more than two variables , can be efficiently approximated by a corresponding Mixed Integer Programming Problem . Abstract This work proposes the use of graph partitioning tools to identify clusters in a possible large space of plausible product formulations , with each cluster representing a significantly different design alternative that deserve to be tested . The partitioning tools are used to find clusters in a large set of previously generated alternatives , and are also incorporated into a mixed-integer programming problem whose solution generates clustered alternatives ( within a feasible space defined by available models and heuristics ) . An example of a cosmetic emulsion is provided . A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones . The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem , known as the master , to generate trial operating configurations . The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model . The proposed methodology 's performance is illustrated by a case study with 99 distributed generators , 208 loads , and 27 storage units . The results are compared with those obtained in other methodologies , namely MINLP , Genetic Algorithm , original Particle Swarm Optimization ( PSO ) , Evolutionary PSO , and New PSO . SiPSO performance is superior to the other tested PSO variants , demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time . Abstract This paper presents an interactive MINLP solver that is particularly suitable for solution of process synthesis , design and analysis problems . The interactive MINLP solver is based on the decomposition based MINLP algorithms , where a NLP sub-problem is solved in the innerloop and a MILP master problem is solved in the outer-loop . The interactive MINLP solver allows a purely equation system mode ( where all equations are given explicitly ) and a simulation mode ( where the process model equations are included and solved by the simulation engine of a simulator ) . Abstract In this paper , we introduce a new generalized multiperiod scheduling version of the pooling problem to represent time varying blending systems . A general nonconvex MINLP formulation of the problem is presented . The primary difficulties in solving this optimization problem are the presence of bilinear terms , as well as binary decision variables required to impose operational constraints . A stochastic inventory approach is proposed and incorporated into a multiperiod two-stage stochastic mixed-integer nonlinear programming ( MINLP ) model to handle uncertainty in demand and loss or addition of customers . This nonconvex MINLP formulation takes into account customer synergies and simultaneously predicts the optimal sizes of customers ’ storage tanks , the safety stock levels , and the estimated delivery cost for replenishments . To globally optimize this stochastic MINLP problem with modest computational time , we develop a tailored branch-and-refine algorithm based on successive piecewise-linear approximation . Abstract In this work , a Mixed-Integer Nonlinear Programming ( MINLP ) modeling framework for integrating short-term Crude-oil Scheduling ( CS ) and mid-term Refinery Planning ( RP ) has been developed and effectively solved by a proposed Lagrangean Decomposition ( LD ) algorithm . The principles of this integration are based on the fact that both Crude-oil Scheduling and Refinery Planning have their economic net values as their objectives , and that they are physically linked by Crude Distillation Units ( CDUs ) . Also , uncertainty of the reservoir capacity and national policies is important factors in practical pipeline deployment , especially in critical conditions mentioned above . In this study , the development of an optimal CCS pipeline network in critical conditions , and the effect of practical design conditions on the network , are studied using mixed-integer nonlinear programming ( MINLP ) modeling and optimization . South Korea is selected as a case study and optimal CCS networks are obtained according to four scenarios developed with regard to reservoir capacity and policy options . Generalized Disjunctive Programming ( GDP ) has emerged as a modeling framework to explicitly represent the relationship between algebraic descriptions and the logical structure of a design problem . However , fewer formulation examples exist for GDP compared to the traditional Mixed-Integer Nonlinear Programming ( MINLP ) modeling approach . In this paper , we propose the use of GDP as a modeling tool to organize model variants that arise due to characterization of different sections of an end-to-end process at different detail levels . It consequently results in a higher accuracy of the model and better computational efficiency . The acquired results demonstrated that the LB-MINLP model , compared to the existing ( heuristic-based and solver-based ) models in the literature , can easily handle the non-smooth and nonlinear terms and achieve an optimal solution much faster and without any outcome violation . It generates inner- and outer-approximations using column generation , which are successively refined by solving many easier MINLP and MIP subproblems in parallel ( using BB ) , instead of searching over one ( global ) BB search tree . We present preliminary numerical results with Decogo ( Decomposition-based Global Optimizer ) , a new parallel decomposition MINLP solver implemented in Python and Pyomo . Thereafter , two rounding algorithms are developed to round the fractional solution based on column-sparse packing and dependent rounding techniques , respectively . Finally , we compare the performance of the proposed algorithms with an optimal MINLP solver through numerical examples . The paper presents the two-phase MINLP topology and standard sizes optimization of a single-storey industrial steel building . The structure consists of main portal frames , connected with purlins . As a practical issue , resource management at different operation levels ( centralized , partially decentralized , decentralized , and fully decentralized ) is discussed . To facilitate the centralized implementation , a more practical version for the I-MINLP algorithm has been developed . The centralized and partially decentralized schemes are preferred for their high performance and flexibility . Multipath transport protocols are aimed at increasing the throughput of data flows as well as maintaining fairness between users , which are both crucial factors to maximize user satisfaction . In this paper , a mixed (non)linear programming ( MINLP ) solution is developed which provides an optimum solution to allocate link capacities in a network to a number of given traffic demands considering both the maximization of link utilization as well as fairness between transport layer data flows or subflows . The solutions of the MINLP formulation are evaluated w. r. t. their throughput and fairness using well-known metrics from the literature . Robots ’ path planners have to optimally balance potentially conflicting goals : keep the traveled distance within an assigned maximum value while , at the same time , let the robot reliably and effectively communicate with other robots in the multi-robot network , and reduce the risk of collisions . We propose a solution approach based on the integration of two components : a link quality predictor based on supervised learning , and a path optimizer , based on a mathematical programming formulation . The predictor is built offline and yields spatial predictions of the expected communication quality of the wireless links in terms of packet reception rate . The penalty term is a function of the amount of dilation from the size of the optimal tree obtained for each group multicast independently from the others ( i.e. , in isolation ) . Since the mathematical programming formulation for the optimization problem is computationally intractable , we resort to suboptimal solutions with heuristics . Our heuristic method aims to reduce the sharing of a link while ensuring that the size of multicast trees will never exceed /spl alpha/OPT/sup k/ where OPT/sup k/ is the size of the optimum tree for multicast group k in isolation . Abstract With the rapid development in computer technologies , mathematical programming-based technique to solve scheduling problems is significantly receiving attention from researchers . Although , it is not efficient solution method due to the NP-hard structure of these problems , mathematical programming formulation is the first step to develop an effective heuristic . Abstract The mathematical programming-based technique data envelopment analysis ( DEA ) has often treated data as being deterministic . In response to the criticism that in most applications there is error and random noise in the data , a number of mathematically elegant solutions to incorporating stochastic variations in data have been proposed . The former level implements an approach for the determination of the optimal turning rates of vehicles traveling within the considered urban area . Basing on these values , in the latter level , a mathematical programming-based methodology determines the optimal duration of the phases of various traffic signaling systems in the considered area . A third level , namely the data collection and state estimation level , is also included in the proposed architecture and discussed in the paper . Mathematical programming can be used to determine , from a set of part-type orders , an input stream composition which maximizes machine utilizations in an FMS ( flexible manufacturing system ) composed of specified machining resources . Simulation can then be used to estimate the degradation in these utilizations due to their dependency on the following factors ignored in the mathematical programming solution : ( 1 ) secondary FMS resources ( e.g. pallets and fixtures ; loading and unloading stations ; buffers ; type and capacity of equipment for transferring work-in-process ) ; ( 2 ) geometric considerations ( e.g. , location of loading and unloading stations , machines , and buffers ; routes for transfer of work-in-process ) ; ( 3 ) secondary time requirements ( e.g. , transfer times ; palletizing and depalletizing times ; fixturing , defixturing , and refixturing times ) ; ( 4 ) operating procedures ( e.g. , quantity of work-in-process ; dispatching rules ; part input sequence ) ; ( 5 ) operating discontinuities ( e.g. , machine breakdowns ; scheduled machine maintenance ; machine substitution ; breakdowns and/or maintenance of equipment for transferring work-in-process ) ; and ( 6 ) secondary job characteristics ( e.g. , the sequence in which parts use machines ; fixturing and refixturing requirements ; due dates ; lateness penalties ) . This paper presents an example illustrating the sequential use of mathematical programming and simulation to : ( 1 ) determine the level of selected secondary FMS resources required to maximize machine utilizations when transfer times are realistic ; ( 2 ) estimate the degradation in machine utilizations when there are inadequate secondary resources ; and ( 3 ) determine the sensitivity of machine utilizations to selected operating procedures . In terms of the solution time , for low density and high node degree topologies the mathematical programming formulation is comparable , and sometimes better , to that of the heuristics . However , in topologies with low node degree and high node density the heuristic solutions outperform the mathematical programming solution . The significance of the work lies in three aspects . First , since linear programming can be solved in polynomial time , the work thus provides , for the first time , a mathematical programming solution with computational efficiency comparable to known combinatorial CVM algorithms . Second , the compact linear programming approach is provably good and natural for general CVM , while previous restricted CVM algorithms are difficult to he extended to the general case . We then integrate such fuzzy chance constraint into the mixed-integer linear programming ( MILP ) model proposed in our previous study to develop a fuzzy chance-constrained programming model . To enable the improved model to be effectively programmed in the standard mathematical programming software and solved by exact solution algorithms , a crisp equivalent linear reformulation of the fuzzy chance constraint is generated . Finally , we modify the empirical case presented in our previous study by replacing the deterministic service capacities with trapezoidal fuzzy ones . Abstract : The emphasis of this research is on the development of mathematical programming tools for the design of S/F communication networks . An analytical model for the system is first presented and discussed . 78(1 ) ( 1994 ) 49–57 ) , who discusses a necessary and sufficient condition for a labor mix to be feasible and presents a simple one-pass method that frequently gives the least cost labor mix . We show in this paper that the integer programming approach is well suited for solving this problem : the definition of the integer programming model is simple , its implementation is immediate by using , for example , the Mathematical programming language ( MPL ) and the integer programming solver XA , the computation times are low ( generally a few seconds on a small microcomputer ) and finally the powerful of the integer programming approach allows us to extend the model in two interesting directions . A new estimation procedure is proposed to estimate the utility parameters and the threshold points simultaneously . This new procedure can be formulated as a linear integer programming model solvable by the standard mathematical programming package and the resulting estimates are maximum likelihood estimates . The proposed framework can be applied to the modeling and the estimation of reservation price and reference price . It is rich in terms of products and production technologies , and has a large transportation problem associated with it . The model is formulated as a cost minimization problem and is solved in terms of several scenarios , each representing a different strategy for capacity expansion , using a mathematical programming package . Therefore explicit consideration is given to the multi-objective nature of the problem . Attempts to solve the problem using a commercial mathematical-programming package failed , but a specially written computer program was run on a mini-computer . In the optimal solution , total travel time is reduced by 15.7 % and total distance by 11.5 % , while the number of empty seats in each vehicle is more equally distributed . This paper focuses on the resolution of the reachability problem in Petri nets , using the logical abstraction technique and the mathematical programming paradigm . The proposed approach is based on an implicit exploration of the Petri net reachability graph . Our heuristic search method uses candidate list strategy , diversification , intensification and strategic oscillation elements . To test our heuristic approach , a set of test problems has been randomly generated and pseudoptimally solved by a mathematical programming software package . Computational results disclose that our tabu search approach is most effective , in terms of both solution quality and efficiency , for larger problem instances .
We have fabricated and successfully tested , for the first time , a prototype chip of a Race Logic computing paradigm , which makes positive use of race conditions for accelerating a broad class of optimization problems , such as ones solved by dynamic programming algorithms . In Race Logic , information is encoded in signal propagation delay , rather than conventional logic levels , and the result of the computation is observed from relative timing differences between injected signals , i.e. the outcome of races .
This paper proposes a generic cross-layer optimization framework for cooperative video summary transmission , which jointly considers the source coding , relay processing parameters , power allocation , and error concealment strategy to achieve the best video quality . The problem is solved by using Lagrangian relaxation and dynamic programming ( DP ) . Experimental results show that the proposed scheme significantly outperforms the direct transmission scheme , the multipath scheme , and the decode-and-forward transmission scheme by up to 25 % .
Reconfigurable systolic arrays can be adapted to efficiently resolve a wide spectrum of computational problems ; parallelism is naturally explored in systolic arrays and reconfigurability allows for redefinition of the interconnections and operations even during run time ( dynamically ) . We present a reconfigurable systolic architecture that can be applied for the efficient treatment of several dynamic programming methods for resolving well-known problems , such as global and local sequence alignment , approximate string matching and longest common subsequence . The dynamicity of the reconfigurability was found to be useful for practical applications in the construction of sequence alignments .
Pure dynamic-programming-based systems are often used for PHMM searches . However , these dynamic-programming- based systems are very time consuming for a large database . For instance , it may take approximately 15 minutes to search a short model of length 12 in the GenBank protein sequence database .
In this paper , we review the main causes of editing errors in literature and propose an editing model relying on a minimization of such errors . We make a plausible semi-Markov assumption , resulting in a dynamic programming solution which is computationally efficient . We also show that our method can generate movies with different editing rhythms and validate the results through a user study .
Measurement of software trustworthiness is an important research field in the software engineering , which is very useful for analyzing the software quality . In this paper , we propose a mathematical programming approach to allocate the trustworthy degree to each sub-attribute of some software attribute appropriately and then to make the trustworthy degree of this attribute maximize under some constraint conditions . Some sufficient or necessary conditions for analyzing this mathematical programming problem are investigated .
Assigning the airport gate with high efficiency is a key task in the airport ground operation . A mathematical programming model was established whose objective function was minimizing the overall variance of slack time(OVST ) to enable the slack time to be uniformly distributed . According to the intrinsic characteristics of the objective function itself , the model was solved by a heuristic algorithm that was designed through rigorous theoretical analysis .
We propose a scenario-based solution approach that can deal with a wide range of uncertainty types . The approach is based on an MILP to find a plan with minimum expected costs over all relevant scenarios . To solve this MILP , we propose an exact branch-and-price algorithm .
The performance of the ICP approach is compared with other alternative approaches , i.e. , nonlinear programming approaches , a mixed-integer nonlinear programming ( MINLP ) approach , and a mixed-integer linear programming ( MILP ) approach . In addition , this paper formulates the real-time train scheduling problem with stop-skipping and shows how to solve it using an MINLP approach and an MILP approach . The ICP approach is shown , via a case study , to provide a better tradeoff between performance and computational complexity for the real-time train scheduling problem .
Therefore , new suitable exploration methods for the 3D thermal-aware floorplaning problem need to be developed . In this paper we analyze the benefits of two different exploration techniques for the floorplanning problem : Multi-Objective Genetic Algorithm ( MOGA ) and a Mixed Integer Linear Program ( MILP ) . We present a novel algorithm that uses MILP to minimize average temperature in the 3D chip , whereas uses MOGA to insert TSVs , connecting the layers while the total wire length is minimized .
An effective cut-generating scheme based on temporal analysis in project scheduling is devised for resolving resource conflicts . The computational study shows that our hybrid MILP/CP algorithm is both effective and efficient compared to the pure MILP or CP method alone .
An application example is considered in which the least-cost schedule has to be derived for processing a set of orders with release and due dates using a set of dissimilar parallel machines . It is shown that this problem can be modeled as an MILP , a CP , a combined MILP-CP OPL model ( Van Hentenryck 1999 ) , and a hybrid MILP/CP model . The computational performance of these models for several sets shows that the hybrid MILP/CP model can achieve two to three orders of magnitude reduction in CPU time .
Finally , these moves have to be sequenced for each crane for processing . In this paper , an integrated MILP model is proposed , which aims to solve the TYSP as a single optimization problem . The proposed formulation also enables us to define more robust and complex objective functions that include key characteristics from each of the above-mentioned subproblems .
A refinement procedure is defined that allows to further improve communications by also minimizing network delays . We report a computational analysis of the integrated MILP model and an evaluation of the impact of a number of parameters on the trade-off between computational load and quality of the output . Results show that the model is computationally affordable for reasonably sized scenarios , and can effectively balance different performance trade-offs .
Finally , the performance of the priority rule based approach and the MILP model are analysed for different scenarios with respect to total lateness of the AGVs . The main focus of the numerical investigation is on evaluating the priority rule based approach for single and dual-load vehicles as well as comparing its performance against the MILP modelling approach .
Abstract The production scheduling problem of a real-life food industry is addressed in this work . An efficient MILP-based solution strategy is developed to optimize weekly schedules for a Spanish canned fish production plant . The multi-stage , multi-product facility under study consists of both continuous and batch operations resulting in an extremely complex scheduling problem .
We also compare our results with a recent RCPSP-specific exact method . We show that , in terms of exact solving , no MILP formulation class dominates the other ones and that a state-of-the art specialized ( non-MILP ) method for the RCPSP is even outperformed by MILP on a set of hard instances . Furthermore , on another set of hard ' ' highly cumulative ' ' RCPSP instances with a high processing time range , our On/Off formulation outperforms all the others MILP formulations and obtains results close to the ones of the specialized method .
In doing so , we look for a general-purpose approach applicable to any MIBLP under mild conditions , rather than ad-hoc methods for specific cases . Our approach concentrates on minimal additions required to convert an effective branch-and-cut MILP exact code into a valid MIBLP solver , thus inheriting the wide arsenal of MILP tools cuts , branching rules , heuristics available in modern solvers .
Abstract This paper describes a model predictive control strategy to find the optimal decision variables to maximize profit in supply chains with multiproduct , multiechelon distribution networks with multiproduct batch plants . The key features of this paper are : ( 1 ) a discrete time MILP dynamic model that considers the flow of material and information within the system ; ( 2 ) a general dynamic optimization framework that simultaneously considers all the elements of the supply chain and their interactions ; and ( 3 ) , a rolling horizon approach to update the decision variables whenever changes affecting the supply chain arise . The paper compares the behavior of a supply chain under centralized and decentralized management approaches , and shows that the former yields better results , with profit increases of up to 15 % as shown in an example problem .
In the bi-level programming models , the number of parking permits assigned or held by each OD pair is an implicit function with respect to parking fee . A global optimum oriented solution algorithm and simulated annealing algorithm are used to solve the MILP and bi-level programming models respectively . Numerical examples are carried out to show that the hybrid schemes with parking pricing and parking permits are more effective than both pure parking pricing scheme and pure parking permits management scheme .
To reduce the calculation complexity of these models , they are transformed into their equivalent fuzzy chance constrained mixed-integer linear programming ( CFCC-MILP ) models . After that these CFCC-MILP models are solved through their crisp equivalent algorithms based on the theory presented by Liu & Liu [ 2002 ] for the first time in refinery scheduling area . Finally , a case study which has 265 continuous variables , 68 binary variables and 318 constraints is effectively solved in LINGO 8.0 with the proposed approaches .
We present mixed integer programming approaches for optimally solving a combinatorial optimization problem arising in network design with additional quality of service constraints . The rooted delay- and delay-variation-constrained Steiner tree problem asks for a cost-minimal Steiner tree satisfying delay-constraints from source to terminals and a maximal variation-bound between particular terminal path-delays .
This paper presents the state-of-the-art on application of optimisation techniques in groundwater quality and quantity management . In order to solve optimisation-based groundwater management models , researchers have used various mathematical programming techniques such as linear programming ( LP ) , nonlinear programming ( NLP ) , mixed-integer programming ( MIP ) , optimal control theory-based mathematical programming , differential dynamic programming ( DDP ) , stochastic programming ( SP ) , combinatorial optimisation ( CO ) , and multiple objective programming for multipurpose management . Studies reported in the literature on the application of these methods are reviewed in this paper .
The problem involves a heterogeneous fleet of vehicles of different capacities , mandatory visits to a subset of vertices , time windows , rest requirements associated with maximum driving and working times , and partial collection . A mixed integer programming formulation of the problem is described , along with three matheuristics based on Tabu Search and Large Neighbourhood Search . Computational results on instances derived from the case study are presented , and insights are given from a practical implementation .
Therefore , in this study two options for each non-hub node are given : ( i ) either it could be visited by a local tour or ( ii ) it could be directly connected to a hub without an economy of scale . We develop a mixed integer programming formulation and strengthen it with valid inequalities . We also develop three different Benders formulations as exact solution methods .
For this problem , we develop a 2-flip neighborhood local search algorithm with a heuristic size reduction algorithm , in which a new evaluation scheme of variables is introduced taking account of GUB constraints . According to computational comparison with the latest version of a mixed integer programming solver , our algorithm performs quite effectively for various types of instances , especially for very large-scale instances .
Furthermore , we argue that an important step in this approach is finding dual alternative optimal solutions to the Benders subproblems and describe an approach to obtain a diverse set of these . Numerical tests show that the described methodology significantly outperforms a commercial Mixed Integer Programming solver on instances with 1241 different shift types and remains competitive for larger cases with 2145 shift types . On all classes of problems the heuristic is able to quickly find good solutions .
This issue has not yet been discussed in the literature . First , a mixed integer programming mathematical model is presented . Then , in order to solve the problem , a new algorithm is proposed which is a new combination of genetic and Simulated Annealing Algorithms .
A scientific and reasonable steelmaking - continuous casting production scheduling is the key to improve the production efficiency of the steelmaking - continuous casting production system . In this paper , with the shortest total charge waiting time and the castings that are processed on time as the optimization goal , with the charges that are casted continuous within same casting and machine capacity constraints and so on as the constraints to established a mixed integer programming mathematical model . The augmented Lagrangian relaxation algorithm is used to solved the scheduling problem .
Our main goal is to integrate offshore platforms from the chemical and petroleum engineering literature , and capacity investment from operations management literature . We use a mixed integer programming solution approach and set a basic model to analyse the value of operational flexibility . Our main contribution is to provide an operational flexibility option to the problem of oil drilling .
The IBM differs from traditional batching formulations by assigning orders to indexed batches , whereby each batch corresponds to a position in the batch release sequence . We develop a mixed integer programming solution for exact control , and demonstrate a simulated annealing procedure to solve large practical problems . Our results indicate that the proposed approach achieves a 5–15 % reduction in the total retrieval time primarily by reducing picker blocking .
The approximation generalizes the widely known model for approximating single variable functions , and significantly expands the set of nonlinear problems that can be efficiently solved by reducing them to Mixed Integer Programming Problems . By our development , any nonlinear programming problem , including non-convex ones , with an objective function ( and/or constraints ) that can be expressed as sums of component nonlinear functions of no more than two variables , can be efficiently approximated by a corresponding Mixed Integer Programming Problem .
Abstract This work proposes the use of graph partitioning tools to identify clusters in a possible large space of plausible product formulations , with each cluster representing a significantly different design alternative that deserve to be tested . The partitioning tools are used to find clusters in a large set of previously generated alternatives , and are also incorporated into a mixed-integer programming problem whose solution generates clustered alternatives ( within a feasible space defined by available models and heuristics ) . An example of a cosmetic emulsion is provided .
A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones . The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem , known as the master , to generate trial operating configurations . The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model .
The proposed methodology 's performance is illustrated by a case study with 99 distributed generators , 208 loads , and 27 storage units . The results are compared with those obtained in other methodologies , namely MINLP , Genetic Algorithm , original Particle Swarm Optimization ( PSO ) , Evolutionary PSO , and New PSO . SiPSO performance is superior to the other tested PSO variants , demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time .
Abstract This paper presents an interactive MINLP solver that is particularly suitable for solution of process synthesis , design and analysis problems . The interactive MINLP solver is based on the decomposition based MINLP algorithms , where a NLP sub-problem is solved in the innerloop and a MILP master problem is solved in the outer-loop . The interactive MINLP solver allows a purely equation system mode ( where all equations are given explicitly ) and a simulation mode ( where the process model equations are included and solved by the simulation engine of a simulator ) .
Abstract In this paper , we introduce a new generalized multiperiod scheduling version of the pooling problem to represent time varying blending systems . A general nonconvex MINLP formulation of the problem is presented . The primary difficulties in solving this optimization problem are the presence of bilinear terms , as well as binary decision variables required to impose operational constraints .
A stochastic inventory approach is proposed and incorporated into a multiperiod two-stage stochastic mixed-integer nonlinear programming ( MINLP ) model to handle uncertainty in demand and loss or addition of customers . This nonconvex MINLP formulation takes into account customer synergies and simultaneously predicts the optimal sizes of customers ’ storage tanks , the safety stock levels , and the estimated delivery cost for replenishments . To globally optimize this stochastic MINLP problem with modest computational time , we develop a tailored branch-and-refine algorithm based on successive piecewise-linear approximation .
Abstract In this work , a Mixed-Integer Nonlinear Programming ( MINLP ) modeling framework for integrating short-term Crude-oil Scheduling ( CS ) and mid-term Refinery Planning ( RP ) has been developed and effectively solved by a proposed Lagrangean Decomposition ( LD ) algorithm . The principles of this integration are based on the fact that both Crude-oil Scheduling and Refinery Planning have their economic net values as their objectives , and that they are physically linked by Crude Distillation Units ( CDUs ) .
Also , uncertainty of the reservoir capacity and national policies is important factors in practical pipeline deployment , especially in critical conditions mentioned above . In this study , the development of an optimal CCS pipeline network in critical conditions , and the effect of practical design conditions on the network , are studied using mixed-integer nonlinear programming ( MINLP ) modeling and optimization . South Korea is selected as a case study and optimal CCS networks are obtained according to four scenarios developed with regard to reservoir capacity and policy options .
Generalized Disjunctive Programming ( GDP ) has emerged as a modeling framework to explicitly represent the relationship between algebraic descriptions and the logical structure of a design problem . However , fewer formulation examples exist for GDP compared to the traditional Mixed-Integer Nonlinear Programming ( MINLP ) modeling approach . In this paper , we propose the use of GDP as a modeling tool to organize model variants that arise due to characterization of different sections of an end-to-end process at different detail levels .
It consequently results in a higher accuracy of the model and better computational efficiency . The acquired results demonstrated that the LB-MINLP model , compared to the existing ( heuristic-based and solver-based ) models in the literature , can easily handle the non-smooth and nonlinear terms and achieve an optimal solution much faster and without any outcome violation .
It generates inner- and outer-approximations using column generation , which are successively refined by solving many easier MINLP and MIP subproblems in parallel ( using BB ) , instead of searching over one ( global ) BB search tree . We present preliminary numerical results with Decogo ( Decomposition-based Global Optimizer ) , a new parallel decomposition MINLP solver implemented in Python and Pyomo .
Thereafter , two rounding algorithms are developed to round the fractional solution based on column-sparse packing and dependent rounding techniques , respectively . Finally , we compare the performance of the proposed algorithms with an optimal MINLP solver through numerical examples .
The paper presents the two-phase MINLP topology and standard sizes optimization of a single-storey industrial steel building . The structure consists of main portal frames , connected with purlins .
As a practical issue , resource management at different operation levels ( centralized , partially decentralized , decentralized , and fully decentralized ) is discussed . To facilitate the centralized implementation , a more practical version for the I-MINLP algorithm has been developed . The centralized and partially decentralized schemes are preferred for their high performance and flexibility .
Multipath transport protocols are aimed at increasing the throughput of data flows as well as maintaining fairness between users , which are both crucial factors to maximize user satisfaction . In this paper , a mixed (non)linear programming ( MINLP ) solution is developed which provides an optimum solution to allocate link capacities in a network to a number of given traffic demands considering both the maximization of link utilization as well as fairness between transport layer data flows or subflows . The solutions of the MINLP formulation are evaluated w. r. t. their throughput and fairness using well-known metrics from the literature .
Robots ’ path planners have to optimally balance potentially conflicting goals : keep the traveled distance within an assigned maximum value while , at the same time , let the robot reliably and effectively communicate with other robots in the multi-robot network , and reduce the risk of collisions . We propose a solution approach based on the integration of two components : a link quality predictor based on supervised learning , and a path optimizer , based on a mathematical programming formulation . The predictor is built offline and yields spatial predictions of the expected communication quality of the wireless links in terms of packet reception rate .
The penalty term is a function of the amount of dilation from the size of the optimal tree obtained for each group multicast independently from the others ( i.e. , in isolation ) . Since the mathematical programming formulation for the optimization problem is computationally intractable , we resort to suboptimal solutions with heuristics . Our heuristic method aims to reduce the sharing of a link while ensuring that the size of multicast trees will never exceed /spl alpha/OPT/sup k/ where OPT/sup k/ is the size of the optimum tree for multicast group k in isolation .
Abstract With the rapid development in computer technologies , mathematical programming-based technique to solve scheduling problems is significantly receiving attention from researchers . Although , it is not efficient solution method due to the NP-hard structure of these problems , mathematical programming formulation is the first step to develop an effective heuristic .
Abstract The mathematical programming-based technique data envelopment analysis ( DEA ) has often treated data as being deterministic . In response to the criticism that in most applications there is error and random noise in the data , a number of mathematically elegant solutions to incorporating stochastic variations in data have been proposed .
The former level implements an approach for the determination of the optimal turning rates of vehicles traveling within the considered urban area . Basing on these values , in the latter level , a mathematical programming-based methodology determines the optimal duration of the phases of various traffic signaling systems in the considered area . A third level , namely the data collection and state estimation level , is also included in the proposed architecture and discussed in the paper .
Mathematical programming can be used to determine , from a set of part-type orders , an input stream composition which maximizes machine utilizations in an FMS ( flexible manufacturing system ) composed of specified machining resources . Simulation can then be used to estimate the degradation in these utilizations due to their dependency on the following factors ignored in the mathematical programming solution : ( 1 ) secondary FMS resources ( e.g. pallets and fixtures ; loading and unloading stations ; buffers ; type and capacity of equipment for transferring work-in-process ) ; ( 2 ) geometric considerations ( e.g. , location of loading and unloading stations , machines , and buffers ; routes for transfer of work-in-process ) ; ( 3 ) secondary time requirements ( e.g. , transfer times ; palletizing and depalletizing times ; fixturing , defixturing , and refixturing times ) ; ( 4 ) operating procedures ( e.g. , quantity of work-in-process ; dispatching rules ; part input sequence ) ; ( 5 ) operating discontinuities ( e.g. , machine breakdowns ; scheduled machine maintenance ; machine substitution ; breakdowns and/or maintenance of equipment for transferring work-in-process ) ; and ( 6 ) secondary job characteristics ( e.g. , the sequence in which parts use machines ; fixturing and refixturing requirements ; due dates ; lateness penalties ) . This paper presents an example illustrating the sequential use of mathematical programming and simulation to : ( 1 ) determine the level of selected secondary FMS resources required to maximize machine utilizations when transfer times are realistic ; ( 2 ) estimate the degradation in machine utilizations when there are inadequate secondary resources ; and ( 3 ) determine the sensitivity of machine utilizations to selected operating procedures .
In terms of the solution time , for low density and high node degree topologies the mathematical programming formulation is comparable , and sometimes better , to that of the heuristics . However , in topologies with low node degree and high node density the heuristic solutions outperform the mathematical programming solution .
The significance of the work lies in three aspects . First , since linear programming can be solved in polynomial time , the work thus provides , for the first time , a mathematical programming solution with computational efficiency comparable to known combinatorial CVM algorithms . Second , the compact linear programming approach is provably good and natural for general CVM , while previous restricted CVM algorithms are difficult to he extended to the general case .
We then integrate such fuzzy chance constraint into the mixed-integer linear programming ( MILP ) model proposed in our previous study to develop a fuzzy chance-constrained programming model . To enable the improved model to be effectively programmed in the standard mathematical programming software and solved by exact solution algorithms , a crisp equivalent linear reformulation of the fuzzy chance constraint is generated . Finally , we modify the empirical case presented in our previous study by replacing the deterministic service capacities with trapezoidal fuzzy ones .
Abstract : The emphasis of this research is on the development of mathematical programming tools for the design of S/F communication networks . An analytical model for the system is first presented and discussed .
78(1 ) ( 1994 ) 49–57 ) , who discusses a necessary and sufficient condition for a labor mix to be feasible and presents a simple one-pass method that frequently gives the least cost labor mix . We show in this paper that the integer programming approach is well suited for solving this problem : the definition of the integer programming model is simple , its implementation is immediate by using , for example , the Mathematical programming language ( MPL ) and the integer programming solver XA , the computation times are low ( generally a few seconds on a small microcomputer ) and finally the powerful of the integer programming approach allows us to extend the model in two interesting directions .
A new estimation procedure is proposed to estimate the utility parameters and the threshold points simultaneously . This new procedure can be formulated as a linear integer programming model solvable by the standard mathematical programming package and the resulting estimates are maximum likelihood estimates . The proposed framework can be applied to the modeling and the estimation of reservation price and reference price .
It is rich in terms of products and production technologies , and has a large transportation problem associated with it . The model is formulated as a cost minimization problem and is solved in terms of several scenarios , each representing a different strategy for capacity expansion , using a mathematical programming package .
Therefore explicit consideration is given to the multi-objective nature of the problem . Attempts to solve the problem using a commercial mathematical-programming package failed , but a specially written computer program was run on a mini-computer . In the optimal solution , total travel time is reduced by 15.7 % and total distance by 11.5 % , while the number of empty seats in each vehicle is more equally distributed .
This paper focuses on the resolution of the reachability problem in Petri nets , using the logical abstraction technique and the mathematical programming paradigm . The proposed approach is based on an implicit exploration of the Petri net reachability graph .
Our heuristic search method uses candidate list strategy , diversification , intensification and strategic oscillation elements . To test our heuristic approach , a set of test problems has been randomly generated and pseudoptimally solved by a mathematical programming software package . Computational results disclose that our tabu search approach is most effective , in terms of both solution quality and efficiency , for larger problem instances .
A 4-mm2 180-nm-CMOS 15-Giga-cell-updates-per-second DNA sequence alignment engine based on asynchronous race conditions Cooperative video summary transmission in wireless networks Reconfigurable Systems for Sequence Alignment and for General Dynamic Programming Sequence Homology Search Based on Database Indexing Using the Profile Hidden Markov Model Continuity Editing for 3D Animation An Approach of Trustworthy Measurement Allocation Based on Sub-Attributes of Software Gate assignment modeling and heuristic algorithm design Scenario-based approach for flexible resource loading under uncertainty Efficient Real-Time Train Scheduling for Urban Rail Transit Systems Using Iterative Convex Programming A combination of evolutionary algorithm and mathematical programming for the 3d thermal-aware floorplanning problem Scheduling projects with multi-skilled personnel by a hybrid MILP/CP benders decomposition algorithm Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems An integrated model for the transshipment yard scheduling problem Simultaneous task allocation, data routing, and transmission scheduling in mobile multi-robot teams Dispatching multi-load AGVs in highly automated seaport container terminals Optimal production scheduling of food process industries Event-based MILP models for resource-constrained project scheduling problems Intersection Cuts for Bilevel Optimization A model predictive control strategy for supply chain optimization A hybrid management scheme with parking pricing and parking permit for a many-to-one park and ride network Credibility Fuzzy Chance Constrained Models for Crude Oil Scheduling and Storage Management On Solving the Rooted Delay- and Delay-Variation-Constrained Steiner Tree Problem Application of optimisation techniques in groundwater quantity and quality management Matheuristics for solving a multi-attribute collection problem for a charity organisation Hub location and routing problem A Heuristic Algorithm for the Set Multicover Problem with Generalized Upper Bound Constraints A Benders decomposition-based matheuristic for the Cardinality Constrained Shift Design Problem زمانبندی تولید و حملونقل و تخصیص سفارشها در زنجیرۀ تأمین Research on Steelmaking-Continuous Casting Production Scheduling Problem Based on Augmented Lagrangian Relaxation Algorithm under Multi-Coupling Constraints A Commodity Production Model with Operational Flexibility of Investing Optional Capacity on Offshore Platforms Batch picking in narrow-aisle order picking systems with consideration for picker blocking Piece-wise linear approximation of functions of two variables Clustering alternative product formulations using graphs On the optimal capacity expansion of electric power systems Distributed energy resource short-term scheduling using Signaled Particle Swarm Optimization A novel interactive MINLP solver for CAPE applications A discretization-based approach for the optimization of the multiperiod blend scheduling problem Optimal distribution-inventory planning of industrial gases : II. MINLP models and algorithms for stochastic cases Integration of crude-oil scheduling and refinery planning by Lagrangean Decomposition Practical deployment of pipelines for the CCS network in critical conditions using MINLP modelling and optimization: A case study of South Korea Modern Modeling Paradigms Using Generalized Disjunctive Programming A Logic-Based Mixed-Integer Nonlinear Programming Model to Solve Non-Convex and Non-Smooth Economic Dispatch Problems: An Accuracy Analysis. Decomposition-based Inner- and Outer-Refinement Algorithms for Global Optimization Resource Allocation Algorithms Supporting Coexistence of Cognitive Vehicular and IEEE 802.22 Networks The Two-phase MINLP Optimization Of ASingle-storey Industrial Steel Building Modeling and resource management in wireless multimedia wcdma systems A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport Use of Time-Dependent Spatial Maps of Communication Quality for Multi-robot Path Planning The multicast packing problem Evaluation of mathematical models for flexible job-shop scheduling problems Data envelopment analysis with stochastic variations in data Integration of decision levels for traffic management in urban areas Using mathematical programming and simulation to study FMS machine utilizations A Mathematical Solution to the MCDS Problem for Topology Construction in Wireless Sensor Networks Solving constrained via minimization by compact linear programming How Uncertain Information on Service Capacity Influences the Intermodal Routing Decision: A Fuzzy Programming Perspective The design of store-and-forward (s/f) networks for computer communications Integer programming to schedule a hierarchical workforce with variable demands Stochastic preference modeling within a switching regression framework Long term planning of a major national sector using a linear programming model Optimal Solution of a Vehicle-routeing Problem: Transporting Mentally Handicapped Adults to an Adult Training Centre Solving the Petri Nets Reachability Problem Using the Logical Abstraction Technique and Mathematical Programming A Tabu Search Approach for the Tool Assignment and Machine Loading Problem in Flexible Manufacturing Systems
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