Florian Diehlmann

Facility Location Planning in Relief Logistics

# FACILITY LOCATION PLANNING IN RELIEF LOGISTICS

DECISION SUPPORT FOR GERMAN AUTHORITIES

Florian Diehlmann

# **Facility Location Planning in Relief Logistics**

Decision Support for German Authorities

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Karlsruher Institut für Technologie (KIT) Institut für Industriebetriebslehre und Industrielle Produktion Deutsch-Französisches Institut für Umweltforschung

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# **Facility Location Planning in Relief Logistics**

Decision Support for German Authorities

by Florian Diehlmann

Karlsruher Institut für Technologie Institut für Industriebetriebslehre und industrielle Produktion u. Deutsch-Französisches Institut für Umweltforschung

Facility Location Planning in Relief Logistics: Decision Support for German Authorities

Zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften von der KIT-Fakultät für Wirtschaftswissenschaften des Karlsruher Instituts für Technologie (KIT) genehmigte Dissertation

von Florian Diehlmann, M.Sc.

Tag der mündlichen Prüfung: 29. März 2021 Erster Gutachter: Prof. Dr. Frank Schultmann Zweiter Gutachter: Prof. Dr. Hagen Lindstädt

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ISSN 2194-2404 ISBN 978-3-7315-1120-5 DOI 10.5445/KSP/1000136060

# **Acknowledgements**

I want to use this opportunity to acknowledge and thank the people and institutions supporting me during the time I was working on the present dissertation.

First of all, I want to thank my supervisor Prof. Dr. Frank Schultmann and Dr. Marcus Wiens, the head of my lab, for their support over the last years and many fruitful discussions. Also, a huge thank you to Prof. Dr. Hagen Lindstädt, Prof. Dr. Kay Mitusch, and Prof. Dr. Alexander Mädche for reviewing my dissertation and participating in the examination committee, as well as to Eve-Marion Weiss for her support during the dissertation process's administrative steps.

Moreover, I want to thank the members of the Risk Management Lab at the Institute for Industrial Production (IIP) - Farnaz, Florian, Markus, Max, Miriam, Rebecca, and Sascha - for hilarious group meetings, events, and moments! Also, a huge thank you to Katharina for amazing work as a Hiwi, Andreas and Tobias for their support during my rst steps in academia, as well as to Chris, Marco, Nico, and all the other colleagues and friends at IIP for constantly supporting me and contributing to a highly productive and inspiring working environment.

The present dissertation strongly builds upon my research in the BMBF-Project "NOLAN" (BMBF grant number 13N14457). I want to express my gratitude for the inspiring collaboration to the project partners from 4ow (Alex, Eric, Ole, Wendelin) and the TU Dresden (PD Dr. Athanasios Gromitsaris, Kathleen, and Prof. Dr. Martin Schulte). Also, I want to emphasize the importance of my collaborators during dierent research projects - Hannah, Lotte, Max, and Patrick - as well as the students I was privileged to supervise during my time at IIP, which provided me with valuable ideas and inspiration. Furthermore, I acknowledge the support by the KHYS networking grant, supporting my research visit to MIT in October 2019.

Additionally, I want to thank Alex, Paul, and Philipp for proofreading the thesis and providing me with valuable suggestions and feedback, as well as Stephan and Steen for their input and ideas regarding my defense presentation.

Finally, I want to thank my family and friends for their ongoing support throughout the years, without which I would have never been able to achieve what I have achieved so far. I really appreciate everything you did and do for me, and I feel lucky to have you.

Karlsruhe, May 2021

Florian Diehlmann

# **Abstract**

Disasters have devastating impacts on societies, aecting millions of people and businesses each year. The delivery of essential goods to beneciaries in the aftermath of a disaster is one of the main objectives of relief logistics. In this context, selecting suitable locations for three dierent types of essential facilities is central: warehouses, distribution centers, and points of distribution. The present dissertation aims to improve relief logistics by advancing the location selection process and its core components.

Five studies published as companion articles address substantial aspects of relief logistics. Despite the case studies' geographical focus on Germany, valuable insights for relief logistics are derived that could also be applied to other countries. Study A addresses the importance of public-private collaboration in disasters and highlights the signicance of considering dierences in resources, capabilities, and strategies when using logistical models. Moreover, power dierences, information sharing, and partner selection also play an important role. Study B elaborates on the challenges to identify candidate locations for warehouses, which are jointly used by public and private actors, and suggests a methodology to approach the collaborative warehouse selection process. Study C investigates the distribution center selection process and highlights that including decision-makers' preferences in the objective function of location selection models helps to raise awareness of the implications of location decisions and increases transparency for decision-makers and the general population. Study D analyzes the urban water supply in disasters using a combination of emergency wells and mobile water treatment systems. Selected locations of mobile systems change signicantly if vulnerable parts of the population are prioritized. Study E highlights the importance of accurate information in disasters and introduces a framework that allows determining the value of accurate information and the planning error due to inaccurate information.

In addition to the detailed results of the case studies, four general recommendations for authorities are derived: First, it is essential to collect information

before the start of the disaster. Second, training exercises or role-playing simulations with companies will help to ensure that planned collaboration processes can be implemented in practice. Third, targeted adjustments to the German disaster management system can strengthen the country's resilience. Fourth, initiating public debates on strategies to prioritize parts of the population might increase the acceptance of the related decision and the stockpiling of goods for the people who know in advance that they will likely not receive support.

The present dissertation provides valuable insights into disaster relief. Therefore, it oers the potential to signicantly improve the distribution of goods in the aftermath of future disasters and increase disaster resilience.

# **Contents**

# **List of Figures**

# **List of Tables**

# **Part I.**

# **Framework, Foundations and Implications**

# **1. Introduction and Motivation**

The rst wave of the COVID-19 pandemic highlighted the vulnerability of supply chains during disasters in an unprecedented way. All over the world, shortages for a large variety of products were reported in the media, from toilet-paper (The Washington Post 2020) and disinfection liquids (abcNEWS 2020) to clinical ventilators (The New York Times 2020).

As a response to these shortages, German authorities stepped up, collaborated closely with organizations and companies from the private sector, and implemented measures to ensure the population's supply. For example, authorities supported farmers ying in desperately needed seasonal workers (DW 2020b), purchased medical goods such as masks (The Local 2020) or ventilators (Reuters 2020), or distributed goods to vulnerable parts of the population with the help of the Bundeswehr (DW 2020f). Moreover, the installation of dierent types of facilities was a critical component of their relief intervention. At the beginning of the pandemic, these facilities were, for instance, mobile test facilities to conduct tests for Sars-Cov-2 (DW 2020e), warehouses to store a large number of masks (Merkur 2020), or temporary hospitals to oer additional space for critical care patients (DW 2020a). Later, issues such as the location of vaccination centers became relevant (DW 2020d).

The selected facility locations strongly aect the outcome of the intervention. For instance, a remotely located temporary hospital will most likely not ll with patients. Moreover, it will be dicult to supply it with essential goods. Similarly, a warehouse without access to the road network would not be an ecient selection. Therefore, scientic studies optimizing location decisions in disasters provide the opportunity to improve relief logistics. For example, Moline, Goentzel, and Gralla (2019) show that applying a dedicated decision support tool for opening and operating temporary disaster recovery centers can reduce average costs by 75%. However, integrating knowledge from scientic studies supporting such location decisions is exceptionally challenging. According to Kovács and Moshtari (2019), this may be due to a

vast majority of available studies being too abstract and assumption-based. Moreover, the combination of the unique disaster situation, high time pressure, and the time it takes to adjust existing models further increase complexity (Kovács and Moshtari 2019). Consequently, optimization models that support location decisions are more likely to prevail in the practical world if they are developed specically for an anticipated decision. Additionally, they need to include the local specicities, dynamics, and available resources (Kovács and Moshtari 2019).

In this context, the project "NOLAN"1 was initiated, aiming to improve the German population's supply with critical goods during disasters. It is funded by the German Federal Ministry of Education and Research (BMBF) and coordinated by the Institute for Industrial Production at the Karlsruhe Institute of Technology (IIP 2020). One key component of the project is to support German authorities in their logistical decision making. The present dissertation, together with the companion articles, has been developed in the context of this project and was inspired by numerous discussions and workshops with experts from German authorities and the private sector.

The dissertation aspires to support decision-makers before and during disasters. Moreover, disaster resilience is increased with the help of ve studies addressing German disaster management authorities' location decisions. These ve studies assess the selection of dierent facilities along the relief chain2 – from warehouses via distribution centers to distribution points. The studies provide several approaches for decision-makers faced with these concrete decisions. Furthermore, indirect implications can be generalized into four cross-study topics, which lead to managerial recommendations for disaster management authorities. Consequently, the dissertation provides essential insights for researchers and practitioners and can, thereby, improve resilience against future disasters.

The dissertation is structured as follows: Chapter 2 includes an overview of related terms and literature. Chapter 3 outlines the research objectives of the dissertation. The results of the individual studies are highlighted in chapter 4, before the managerial implications of the studies are discussed in chapter

1 "Scalable Emergency Logistics for Urban Areas as Public-Private Emergency Collaboration," in German: "Skalierbare Notfall-Logistik für urbane Räume als Public-Private Emergency Collaboration."

2 An overview of the theoretical background of relief chains follows in chapter 2.4

5. Finally, the dissertation is summarized and critically examined in chapter

6. The companion articles are attached at the end of the dissertation.

# **2. Theoretical Foundation**

# **2.1. Classification of Disasters**

The term "disaster" can be derived from Latin, being translated as "away from the stars" (Coppola 2007, p. 25). From a superstitious perspective, an unlucky constellation of the stars leads to drastic consequences (Eshghi and Larson 2008). According to the International Federation of Red Cross and Red Crescent, "a disaster is a sudden, calamitous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community's or society's ability to cope using its own resources. Though often caused by nature, disasters can have human origins" (IFRC 2020).

The classication characteristics of disasters include the root of the disaster and the time it takes for the disaster to unfold (Coppola 2007). In this context, disasters can either be triggered by technological malfunctions or occur naturally (EM-Dat 2020). Examples of technological disasters are explosions in chemical plants or nuclear reactors. Natural disasters include, inter alia, landslides or hurricanes. Considering the time it takes for a disaster to unfold, sudden- and slow-onset disasters are distinguished (Gupta et al. 2016). Sudden-onset disasters include earthquakes and ash-oods, while slow-onset disasters include droughts and famine. Depending on the disaster context, a variety of actors can become active and play an essential role.

# **2.2. Active Organizations in Disasters**

The activity of dierent actors strongly depends on the local characteristics of the disaster area. While a less developed country like Mozambique required much international support in the aftermath of Cyclone Idai (UNOCHA 2020), the economically more mature United States coped with recent hurricanes

primarily on their own – especially due to a strong public and private sector (NASEM 2020). Consequently, disaster research always has to consider the local context and available resources, making the transfer of knowledge challenging.

Considering Germany, which is rarely aected by large-scale natural disasters, public disaster management is based on the principle of subsidiarity and lies in the remit of the federal states (BMI 2015). For example, in the federal state of Baden-Württemberg, either the direct local administration (e.g., mayors' oces or the oces of the district administration), the "Regierungspräsidium" (the regional council), or the Ministry of the Interior Baden-Württemberg (MIDMBW 2020) are responsible. The responsible organization is mainly determined by the disaster's geographic concentration (MIDMBW 2020). If more than one district is aected, responsibility shifts towards the Regierungspräsidium. If more than one Regierungspräsidium is aected, the Ministry of the Interior is in charge. Moreover, the escalation stage of the local administration is bypassed in case of an incident in the vicinity of nuclear facilities (MIDMBW 2020).

Due to the dierent resources and shifts of responsibilities during an escalating disaster, the collaboration between dierent agencies and bureaus is necessary to account for departments' dierent resources and expertise. This process of mutual support for each other is organized with "Amtshilfe" applications, formalizing responsibilities and costs of the collaboration (see, for instance, BMI (2015) for an overview of the process for inter-state disaster assistance). Moreover, the German government installed the Federal Oce of Civil Protection and Disaster Assistance (BBK) as a federal agency subordinate to the Federal Ministry of the Interior. The BBK is in charge of "matters related to civil protection and disaster assistance" (BMI 2020). For example, they support preparedness measures (BBK 2020) or provide information and coordination during disasters with their Joint Information and Situation Centre (GMLZ; BMI (2020)).

The scientic literature underlies the BBK's signicant role by stressing the importance of coordination and collaboration of all involved actors, even though each actor of an emergency may play a crucial role for a successful intervention (Balcik, Beamon, et al. 2010). For example, Holguín-Veras, Jaller, et al. (2012) argue that even though donors have good intentions, their donations often do not address the population's actual needs. Delivering these goods to disaster areas leads to congestion of transportation infrastructure

and, consequently, inhibits the supply of essential goods. At the same time, humanitarian organizations cannot oend donors by turning down oered goods (Holguín-Veras, Jaller, et al. 2012). Another example is the dierent legal power. While authorities can respond to a disaster by adjusting rules and regulations, logistics service providers need to follow these specications. Even though they can try to achieve legal adjustments through lobbying, coordination is essential to avoid disruptions in supply chains.

The collaboration of authorities and private actors during emergencies is essential since both actors play a central role in supplying the population (Nurmala, Leeuw, and Dullaert 2017; Nurmala, Vries, and Leeuw 2018). In ordinary times, private actors supply goods to the population. Following Diehlmann, Lüttenberg, et al. (2020, p. 8), private actors include all "rms involved in the supply of essentials like food or medicine (e.g., producers, retailers, or logistics service providers)." On the other hand, authorities become responsible during disasters. Thereby, the term "authorities" refers to all public actors, dened as "institutions and organizations under the control of public authorities on a federal and/or provincial level" (Diehlmann, Lüttenberg, et al. 2020, p. 7).

Collaboration between public and private actors falls under the umbrella-term of Public-private Emergency Collaboration (PPEC; Wiens et al. (2018)). PPECs are necessary since, in many cases, public or private actors cannot cope with a disaster by themselves. This is, inter alia, caused by the dierent periods each actor becomes active. For example, the locations of supermarkets are selected to provide goods to the population in ordinary times. On the other hand, a disaster might come with limited mobility of the population, demanding shorter distances between people and points of distribution. Therefore, dierent suggestions for maximum walking distances in disasters exist in the literature (EPA 2011; Fischer and Wienand 2013). For example, the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety denes the maximum walking distance as 1 km (BMUB 2014). However, the average distance between a person and a supermarket in Germany is above 3 km (Neumeier 2014), indicating that the private sector's distribution infrastructure will not be sucient and collaboration necessary.

Due to the signicant impact of disasters on society, combined with the variety of disaster types, practitioners and researchers need to prepare for potential disasters, manage the response towards disasters eciently, and learn from past disasters. These processes are classied in the so-called Disaster Management Cycle (DMC), which is a core component of disaster management literature (H. Khan, Vasilescu, and A. Khan 2008).

# **2.3. The Disaster Management Cycle**

The following four repeating phases dene the DMC (Coppola 2007):1 Response, Recovery, Mitigation, and Preparedness (see Figure 2.1).

Figure 2.1: Overview of the Disaster Management Cycle.

The Response-Phase starts right after the impact of a disaster, focusing on providing relief to the aected area. After the immediate consequences of the disaster are survived, the Recovery-Phase becomes paramount, aiming to "returning victims' lives back to a normal state" (Coppola 2007, p. 8), or even to "build back better" (Mannakkara, Wilkinson, and Francis 2014, p. 1). While it might take months or years to go through the Recovery-Phase (Coppola 2007), the following Mitigation-Phase addresses the impact of potentially upcoming disasters, mitigating the eect of future incidents. Finally, the Preparedness-Phase's objective is to reduce the impact of a specic upcoming disaster (Coppola 2007).

1 Note that the phases uently go into each other and may overlap. Furthermore, a variety of disciplines (for example, sociology or psychology) aected the DMC, leading to unclear origins of the concept (Coetzee and van Niekerk 2012), as well as some variations within the naming of the DMC's components (for example, some scholars prefer the name Prevention-Phase instead of the more frequently used term Mitigation-Phase (Baldini et al. 2011; BMIAT 2020)).

Around 80% of the costs of the immediate disaster response is caused by logistics (van Wassenhove 2006), indicating the importance of ecient logistics management. According to Christopher (2016, p. 2), logistics can be dened as "the process of strategically managing the procurement, movement and storage of materials, parts and nished inventory (and the related information ows) through the organisation and its marketing channels in such a way that current and future protability are maximised through the cost-eective fullment of orders."

The stream of research focusing on logistics in the context of disasters falls under the umbrella term of Humanitarian Logistics. Like traditional business logistics, the management of the ow of goods, information, and nances is central in this stream (Kovács and Spens 2007). However, humanitarian logistical models need to address the particular characteristics of disasters. These characteristics are, for instance, the pursuit of (partially) non-nancial objectives, the lack of information, or the often "once-in-a-lifetime"-periodicity of decisions (in contrast to repetitive decisions in business logistics; Holguín-Veras, Jaller, et al. (2012, p. 499)).

Humanitarian Logistics is central to two dierent types of disaster operations: continuous aid and disaster relief (Kovács and Spens 2007). Continuous aid refers to continuous support during long-lasting crises like providing supplies to a refugee camp. In contrast, disaster relief refers to the response in the aftermath of a disaster. Thus, relief logistics includes all logistical aspects of providing goods during disaster relief. The present dissertation aspires to support decision-makers during their relief logistics decisions. Consequently, the next subsection's focus is on the related key components.

# **2.4. Relief Logistics**

According to Suzuki (2012), relief logistics consists of three major streams: network ow problems, inventory management, and facility location problems. While "network ow problems consist of supply and demand points, together with several routes that connect these points and are used to transfer the supply to the demand" (Bertsekas 1998, p. 2), inventory management refers to "planning and controlling inventory from the raw material stage to the customer" (Arnold, Chapman, and Clive 2008, p. 254). Moreover, facility location problems focus on "determining the 'best' location for one or several

facilities or equipments in order to serve a set of demand points" (Laporte, Nickel, and Saldanha da Gama 2019, p. 1).

Depending on the relief network's considered layout, a plethora of facility types and decisions can be analyzed. In general, a relief network consists of Central Warehouses, which represent the origin of the goods in the network, and the Points of Distribution (PoDs), where beneciaries receive goods. Moreover, Distribution Centers can serve as auxiliary infrastructure, e.g. as pre-positioning locations in anticipation of a disaster (Cotes and Cantillo 2019) or as cross-docks for transportation (see also Figure 2.2). Multiple variations of this basic setting exist in the literature – for instance, including a relief chain without additional distribution centers (Balcik and Beamon 2008), or taking warehouses out of the scope when addressing last-mile optimizations (Noyan, Balcik, and Atakan (2016); see also Anaya-Arenas, Renaud, and Ruiz (2014) for a systematic review).

Figure 2.2: Overview of a generic relief chain.

The selection of appropriate facility locations along the relief chain is challenging. However, there is already a variety of studies addressing dierent decisions. For example, Charles et al. (2016) analyze optimal locations for

global warehouses of an international organization, while Khayal et al. (2015) present a tool to select locations for temporary distribution centers. Moreover, Moreno et al. (2018) introduce an approach to select PoDs under consideration of uncertainties.

Nevertheless, multiple research areas have not been studied thoroughly yet. Therefore, additional studies can improve relief logistics further. An overview of the research objectives that are pursued within the present dissertation, together with related relief decisions, follows in the next chapter. A detailed presentation of the studies' results follows in chapter 4.

# **3. Research Objectives**

The present dissertation aims to contribute to the body of literature and improve the outcome of future relief interventions with the help of ve scientic studies. The following section provides a brief overview of the addressed research objectives. Afterward, section 3.2 locates the studies within the relief chain.

# **3.1. Opportunities to Improve Relief Logistics**

### **3.1.1. Increase Knowledge About Public-Private Collaborations**

As described in section 2.2, various actors become active during disaster relief. These actors contribute to the disaster response depending on their individual motivation, objectives, and resources. In this context, the role and importance of the dierent groups of actors have been studied intensively (see, e.g., Kovács and Spens (2007) and Balcik, Beamon, et al. (2010)). The ecient collaboration of all actors involved is key to relief logistics (Balcik, Beamon, et al. 2010). This becomes especially important considering PPECs (Nurmala, Leeuw, and Dullaert (2017), Nurmala, Vries, and Leeuw (2018), and Wiens et al. (2018); see also section 2.2). However, there are no frameworks or general studies that address possible logistical components that should be included in quantitative PPEC models. Moreover, the identication of these components can be a catalyst for future PPEC research. This research objective is pursued in Study A.

# **3.1.2. Support Decision-Makers Selecting Locations for Warehouses**

The locations of warehouses strongly aect the impact of relief logistics. In this context, location factors play a central role. Location factors represent the factors that aect the decision for a new facility (Schmenner, Huber, and Cook 1987). Many studies already elaborated on dierent factors in the commercial context, e.g., focusing on dierent industry sectors (Saracoglu 2020) or countries (Ramírez-Alesón and Fleta-Asín 2016). Moreover, several studies analyzed location factors in the humanitarian context (see, for example, Dekle et al. (2005) and Roh, Jang, and Han (2013)). However, there is no study contrasting location factors from both perspectives, comparing dierent regions' attractiveness. This is remarkable since these results are of high value for disaster authorities and private actors that want to collaborate. Moreover, there is no methodology available to identify potential locations for jointly used (warehouse) facilities. Study B approaches this research objective.

### **3.1.3. Include Preferences in Objective Functions**

Appropriate objective functions are highly debated in the eld of humanitarian logistics. While the rst humanitarian logistics studies applied commercial objectives towards humanitarian contexts, novel approaches allow researchers to account for the special characteristics of a disaster response (Holguín-Veras, Jaller, et al. 2012; Holguín-Veras, Pérez, et al. 2013). One of these novel approaches is considering the concept of social costs, which was introduced to the humanitarian sector by Holguín-Veras, Jaller, et al. (2012). The main dierence to the commercially used approaches is the combination of a proxy for human suering, so-called deprivation costs, with logistics costs. However, several limitations inhibit applying the model in studies (see, for example, Shao et al. (2020) for a recent discussion of the advantages and disadvantages of deprivation costs). Consequently, extending the concept of social costs can facilitate the application of the approach and, therefore, allow researchers and decision-makers to better account for humanitarian specicities in their models. While there are multiple opportunities to extend the concept, an option with high relevance for practitioners is to include decision-makers' preferences with the help of a weighted-sum approach. This research gap is approached in Study C.

### **3.1.4. Take Advantage of Synergies Utilizing All Available Resources**

Researchers developed various approaches to dene humanitarian supply chains (see, for example, Charles et al. (2016) and Balcik and Beamon (2008)). In particular, humanitarian water supply chains received much attention. For example, Clasen and Boisson (2006) investigate the application of householdbased ceramic water lters, Dorea, Bertrand, and Clarke (2006) the application of clariers on the community level, or Garsadi et al. (2009) the application of mobile water treatment systems. However, there is no study that focused on the combination of mobile treatment systems and the already installed local emergency infrastructure. An example of local infrastructure is the German emergency well system, consisting of grid-independent wells distributed all over Germany. In Berlin, more than 900 wells can provide water during emergencies (Fischer and Wienand 2013).

Combining the available resources is challenging since it emphasizes the problem of prioritization. A supply chain based on non-mobile wells oers little exibility. Even though the well network has been extended in the last decades, the well system was initially designed during the cold war. Consequently, the t with the local demographics is highly questionable. On the other hand, a large amount of water can be distributed via wells.

In contrast, mobile systems oer high exibility, while the total amount of water that can be distributed through them is comparably low. The combination of both technologies oers the chance to supply a large amount of water to the general population while prioritizing vulnerable groups. However, the consequences for the general population that arise from the prioritization of vulnerable people need to be well-understood to make such an important decision. Study D contributes to this stream of research.

### **3.1.5. Collect More Accurate Information**

Accurate information is crucial for successful disaster interventions (Altay and Labonte 2014; Celik and Corbacioglu 2010; Day, Junglas, and Silva 2009). When it comes to the demand for critical goods, much knowledge lies within the hands of private actors, who interact with their customers regularly. On the other hand, authorities need to obtain access to accurate information – for

example, by conscating or purchasing information (as done by the US FEMA (Federaltimes 2019)). Even though research agrees that uncertainty plays an important role in relief logistics (Ahmadi, Sei, and Tootooni 2015; Charles et al. 2016; Vahdani et al. 2018), the eects of dierent information accuracy levels remain unclear. Simultaneously, there is no approach for quantifying the eects of dierent intervention strategies on the private sector if the decision is based on accurate or inaccurate information. Understanding these eects is the objective of Study E.

# **3.2. Addressed Decisions Within the Relief Chain**

The studies address dierent decisions along the relief chain (see also Figure 2.2). Study A provides a general framework and insights relevant to decisions on each relief chain stage. Moreover, Study B supports the warehouse location decision, and Study C aims to identify locations of distribution centers. Furthermore, Studies D and E suggest locations where critical goods can be distributed during disaster relief.

Moreover, ecient facility location decisions in relief logistics are always accompanied by a multitude of preconditions and consequential implications. Even though these aspects are often not directly considered in logistics problems, they strongly aect the success of facility location decisions. The study-based design allows deriving managerial recommendations regarding four of such general cross-study topics: Information Management, Company Involvement, German Specities, and Trade-os.

Information Management refers to the importance of information in disasters, while Company Involvement aims to increase the understanding, how authorities can better involve companies in their relief interventions. Moreover, German Specicities include implications targeted towards the German infrastructure that follow from the case studies' geographical focus. Finally, all insights that aim to improve the understanding of conicting objectives are collected in the domain of Trade-os.

Table 3.1: Overview on the contributions of each study to the cross-study topics.

Table 3.1 links the studies to the cross-study topics. A description of the studies and their results follows in the next chapter. The managerial implications derived from the cross-study topics are discussed in chapter 5.

# **4. Summary of Studies and Results**

# **4.1. Study A: A Framework for Public-Private Emergency Collaborations**

The following section refers to the content of the article "Public-Private Collaborations in Emergency Logistics: A Framework based on Logistical and Game-Theoretical Concepts." This article was written in collaboration with Markus Lüttenberg, Lotte Verdonck, Marcus Wiens, Alexander Zienau, and Frank Schultmann, and was published in the KIT-IIP Working Paper Series in Production and Energy as Diehlmann, Lüttenberg, et al. (2020).

### **Study context and contributions**

The study's objective is to provide an overview of PPECs in theory and practice, introduce a framework for the logistical and game-theoretical modeling of PPEC components, and discuss a basic game-theoretical model depicting a simplied PPEC.

As crises of the past have shown, authorities cannot cope with large-scale disasters by themselves (see, for example, the response to Hurricane Katrina in the United States (Department for Homeland Security and Counterterrorism 2006)). While researchers have identied various organizations involved in disaster relief (see section 2.2), Nurmala, Leeuw, and Dullaert (2017) and Nurmala, Vries, and Leeuw (2018) highlight the central role of private actors in disasters. Moreover, Wiens et al. (2018) stress the potential of PPECs. However, no study provides an overview of the state-of-the-art of PPECs, their key components, and essential aspects to consider in quantitative PPEC studies. This gap is addressed in Study A. A presentation of the main results and points of discussion of the study follows.

## **Results and Discussion**

Even though the literature clearly indicates that PPECs are promising components of eective disaster responses, only a minimal number of PPECs are formally dened in reality. While there is no apparent reason for their scarcity, examples from the COVID-19 pandemic highlighted that, nevertheless, PPECs can be introduced spontaneously. An example is the support of private actors during the purchasing process of face masks. After authorities struggled to nd enough suppliers of face masks, they asked private actors to take over negotiations and use their contacts to acquire masks for them (Tagesschau.de 2020). Moreover, private actors got actively involved and started producing critical goods themselves (Focus 2020).

The already established PPECs, on the other hand, tend to target broader preparedness-topics. For example, the German UP-KRITIS, a working group consisting of experts from the public and private sector, aims to increase critical infrastructure resilience and general knowledge exchange (UPKRITIS 2019). A reason for this could be that private companies want to keep their independence and exibility, as indicated by Walmart after they rejected the role of "emergency merchandise supplier" (Chen et al. 2013, p. 136). Therefore, authorities should aim for a collaboration on a voluntary basis without legal commitment if they wanted to set-up a formalized PPEC with an operational focus. In this context, the (intermediate) results of the research project "NOLAN," in which researchers from the Karlsruhe Institute of Technology, the TU Dresden, and the 4ow AG systematically investigate PPECs (IIP 2020), seem to be very promising. The research partners plan to establish the so-called "AK NOLOG," a permanent working group where public and private actors can collaboratively increase the understanding of the operational relief distribution processes, share best practices, get to know each other better, and organize disaster exercises.

In addition to the insights and implications from the discussion of the stateof-the-art of PPECs, the logistical modeling framework provides valuable guidance for researchers and practitioners who want to analyze PPECs quantitatively (Figure 4.1).

From a logistical perspective, dierences in resources, capabilities, and strategies are essential. For example, tensions may arise from competing long-term strategies: While private actors usually follow nancial objectives to satisfy

Figure 4.1: Interdependencies in Public-Private Emergency Logistics (see also Figure A.3).

shareholders or ensure their long-term liquidity, public actors can focus on reducing the population's suering. Moreover, power dierences, information sharing, and partner selection are important aspects to consider. Legal aspects can be examples of power dierences since public actors have the power to adjust the legal context of the intervention (as, for instance, happened all over the world during various partial or full lockdowns as part of the COVID-19 pandemic response). Additionally, the game-theoretical model and example developed in Study A indicate the powerful implications drawn from such quantitative PPEC analyses.

# **4.2. Study B: Selecting Locations for Warehouses in PPECs**

The following section refers to the content of the article "Identifying Joint Warehouse Locations in Public-Private Emergency Collaborations." This article was written in collaboration with Maximilian Löel, Alexander Zienau, Markus Lüttenberg, Marcus Wiens, Stephan Wagner, and Frank Schultmann, and has been submitted to a scientic journal.

### **Study context and contributions**

The study's objective is to compare the suitability of dierent regions as a warehouse location from authorities' and companies' perspectives and to develop a methodology to select locations for shared warehouses. These warehouses can be used to supply goods to the general population during ordinary times and to beneciaries during disasters.

Private actors supply goods to the population with the help of supply chains that continuously aim to increase their eciency. While authorities observe the market in ordinary times, they need to intervene when the supply drops in times of a disaster. For example, German authorities stepped up during the COVID-19 pandemic and, among other things, loosened driving restrictions for logistics providers (BMVI 2020) or supported farmers during the harvest workers' immigration process (BReg 2020). Furthermore, in case of more severe disturbances, authorities can activate goods from the so-called "Zivile Notfallreserve" (eng: "Civil emergency reserve"), in which large amounts of rice or legumes are stored (BLE 2020). Furthermore, the "Bundesreserve Getreide" (eng: "Federal grain reserve") oers large amounts of wheat and other grains (BLE 2020). Even though these goods could provide helpful relief to beneciaries, various operational and legal factors inhibited the activation of these resources (Rexroth 2010). Consequently, authorities spend much money maintaining goods they barely need (Rexroth 2010).

One approach for increasing the relief chain's exibility and eciency includes involving private actors more directly in disaster preparedness. For instance, this incorporates using their infrastructure and goods in disasters instead of setting up new supply chains. While this requires many operational agreements (for example, an increase of safety stock or the prioritization of deliveries on behalf of authorities), authorities can save money compared to the current, non-cooperative setup by defunding the barely used infrastructure. Hence, part of this money could be spent setting incentives for private actors and, thus, lead to solutions from which both actors prot.

The selection of a location both actors benet from is a challenging task. In the context of this study, it is approached in two ways. With the help of a structured approach, it is possible to determine a region's general attractiveness as a quantitative score using expert interviews, AHP, and selected metrics. Furthermore, this score can be utilized as one of the objectives of a series of mixed-integer linear models with goal-programming objectives. These models suggest the structures of dierent logistics networks using actor-specic combinations of objectives. Following, mapping the dierent networks leads to solutions that are of interest to both actors. These locations can, therefore, be the starting point for discussions on candidate PPEC locations.

Overall, the study contributes to the body of literature in two dierent ways. First, the attractiveness score represents a very exible approach to evaluating and comparing dierent regions' suitability. It can support PPEC-practitioners to understand the required characteristics of the partner better. Second, the introduced models eectively combine multiple objectives and oer a unique perspective of collaborative location selection for disaster preparedness. A presentation of the main results and points of discussion of the study follows.

### **Results and Discussion**

The results of the case study underline the possibilities and exibility of the approach. While the attractiveness score was determined for Germany, the optimization models were restricted to the federal state of Baden-Württemberg to reduce the computational complexity.

Figure 4.2 highlights the scores that result from a total of thirteen interviews with experts from the private sector, authorities, NGOs, and consulting rms.1 These experts assessed nine criteria for their importance. Combined with more than thirty metrics, the nal scores for the attractiveness of a region as a location for a warehouse from the perspectives of the private sector (Figure 4.2a) and relief authorities (Figure 4.2b) result.

While the criteria "Market in Proximity" and "Availability and Skills of Labor Force" have the highest inuence on the commercial attractiveness score, "Availability of Transport Equipment" and "Transport Infrastructure Reliability and Resilience" inuence the emergency score most. Even though regions' attractiveness diers signicantly, it is possible to identify some areas of high attractiveness for both actors. For example, large parts of the federal states of

1 Accounting for inconsistencies in the data, the results of six commercial and six emergency experts were considered in the study.

Nordrhein-Westfalen or Baden-Württemberg seem to be promising options for both actors.

Moreover, the scores are an input of various optimization models. These models determine optimal warehouse network set-ups, considering logistics costs and attractiveness (commercial scenario), or logistics costs, deprivation costs, and attractiveness (emergency scenario). Figure 4.3a highlights the dierent networks. In the commercial scenario, two dierent subscenarios are considered to reect the potential target markets. A market share of 10% represents a situation in which one large food retail chain tries to setup a network. On the other hand, a market share of 100% represents a hypothetical monopolist representing the entire market supply. It follows that a municipality close to Stuttgart is included in each scenario's network, while various locations are a component of two dierent networks. These locations could be candidate locations if both actors wanted to open a new jointly used warehouse.

Furthermore, the exibility of the approach is highlighted with the help of two dierent extensions. In Extension I, the twenty-one warehouses of the largest six German retailers are considered as candidate locations. Figure 4.3b presents the selected locations. Moreover, Extension II regards the hypothetical case that one retailer wanted to extend its network by one warehouse that can be used as an emergency warehouse.

Comparing the results of the extensions, it becomes evident that the number of candidate locations signicantly aects the network's operational performance (measured in costs and deprivation). The network that is based on all retail chains' locations (Extension I) leads to better results than an extension of the warehouses of one chain (Extension II). It can be followed that authorities should aim to increase their exibility by collaborating with multiple chains. Furthermore, this also minimizes market interference by avoiding to "pick winners."

The study comes along with multiple limitations. For example, establishing an entirely new distribution network is a thought experiment that is not likely to happen in reality. Structures develop over a long period and organically, making the extensions considering existing infrastructure more relevant for a practical application of the model. Moreover, individual companies might have dierent strategies and objectives, leading to more complex optimization problems, if applied in practice. Furthermore, disaster dynamics are not included. If, for example, critical infrastructure damage comes with a disaster,

highways might become blocked and negatively aect a location's suitability. Finally, dierent legal conditions, including dierent public administrations and interpretation of responsibilities, are present. Disaster management in Germany is highly localized, while a federal agency runs the emergency warehouses. Therefore, adaptations and adjustments on the country-wide level are only possible if the local disaster plans are adjusted, which will be challenging to coordinate. Nevertheless, the study oers a exible methodology to identify potential locations of collaborative warehouse locations and, therefore, constitutes an essential contribution to increased resilience.

# **4.3. Study C: Selecting Locations for Temporary Distribution Centers**

The following section refers to the content of the article "A Novel Approach to Include Social Costs in Humanitarian Objective Functions." This article was written in collaboration with Patrick S. Hiemsch, Marcus Wiens, Markus Lüttenberg, and Frank Schultmann, and was published in the KIT-IIP Working Paper Series in Production and Energy as Diehlmann, Hiemsch, et al. (2020).

### **Study context and contributions**

The study's objective is to advance the original approach to use social costs (SC) in humanitarian objective functions.

This approach was introduced by Holguín-Veras, Jaller, et al. (2012) and Holguín-Veras, Pérez, et al. (2013), who dene SC as the sum of logistics costs and deprivation costs (DeC). In this context, DeC represent a monetary approximation of human suering (Holguín-Veras, Pérez, et al. 2013), which is often measured via the willingness to pay for a critical good, assuming there is a lack of this good after a disaster (Shao et al. 2020). For example, a person with a water demand of 2 liters per day may survive without water for a limited time. However, it would not be sucient to supply this person with 20 liters of water after 10 days since this person will most likely be dead by then. Moreover, the longer the person is undersupplied, the more severe the physiological implications and the higher the willingness to pay for water. Deprivation cost functions (DCFs) reect these eects and are, therefore, "monotonic, non-linear, and convex with respect to the deprivation time" (Holguín-Veras, Pérez, et al. 2013, p. 267). Consequently, this concept represents a powerful approach to include the escalating suering of the aected population in considerations. Due to the expression of DeCs in monetary units, they can be combined with logistics costs and allow decisionmakers to optimize decisions in terms of both components.

Some disadvantages come along with the application of DCFs in relief optimization models. For example, the functions are susceptible to the local economic context and complex to assess (Shao et al. 2020). Wang et al. (2017) introduce the concept of Deprivation Levels (DL) to solve some of these issues. In contrast to DCFs, Deprivation level functions (DLFs) capture the suering of the population on a dimensionless interval of [0; 10]. While this approach is universal and does not depend on the local economic situation, it is very complex to determine (Shao et al. 2020). Moreover, DLFs cannot be compared to or combined with logistics costs.

Considering this background, a novel method is suggested in Study C. It is based on a normalized weighted sum approach, in which deprivation and logistics cost are normalized with nadir and ideal points (see also Figure C.2). The objective of the resulting optimization problem is to minimize the normalized weighted sum. Therefore, this approach allows decision-makers to include DCFs (or DLFs) and logistics costs into their decisions fast and eciently. Furthermore, the approach allows prioritization of one of the two cost components, dependent on the decision-maker's preferences. An example of such a preference could be an international NGO that can spend every euro saved in one country in another disaster context. Therefore, they might regard reducing human suering as essential but strategically prefer a stronger focus on monetary components in the context of a specic disaster. Consequently, the approach increases transparency regarding decisions and consequences eectively and oers much exibility to decision-makers.

The approach is applied to a case study for hypothetical water contamination in the city of Berlin. Water stored in large warehouses outside the city is distributed at schools all over Berlin. Following Cotes and Cantillo (2019), small temporary distribution centers (DiCs) are opened to distribute the goods (for instance, as cross-docks, as an additional buer, or to merge deliveries from dierent sources). Decision-makers need to select the number and location of these DiCs from a set of twelve candidate locations (large commercial stores or warehouses). This decision is approached with a mixed-integer linear

program that suggests dierent combinations of opened DiCs dependent on decision-makers' preferences. A presentation of the main results and points of discussion of the study follows.

### **Results and Discussion**

The discussion of the results shows the benets of the approach. Figure 4.4 highlights the selected DiCs, dependent on the decision-maker's preference for logistics costs (𝛼 → 1) or deprivation (𝛼 → 0).

Figure 4.4: Opened DiCs for dierent values of 𝛼 (see also Figure C.5).

The gure indicates the importance of dierent DiCs. For example, DiCs 1, 5, and 6 are robust solutions and should be opened in any case. Moreover, DiC 11 is featured in a large number of solutions. On the other hand, DiCs 3 and 8 only become relevant in case of a strong preference for deprivation (𝛼 ≤ 0.1). Furthermore, Figure 4.4 shows the variety of the selected location combinations. Therefore, decision-makers need to understand the consequences of dierent levels of 𝛼 more thoroughly to make a decision.

Figure 4.5 can support this process. Figure 4.5a presents the course of the normalized costs and Figure 4.5b the Pareto-Front. As indicated in Figure 4.5a, deprivation does not signicantly change for 𝛼 ≤ 0.5, while logistics costs almost double in this interval. Simultaneously, logistics costs are comparably stable for 𝛼 ≥ 0.6, while the contribution of the deprivation component does not signicantly change. Figure 4.5b further highlights the total costs,

Figure 4.5: Normalized values and Pareto-Front of the cost components for dierent values of 𝛼 (see also Figures C.6 and C.7).

indicating that DeC are much higher than logistics costs. Consequently, a reduction of DeC in the range of 1% already reduces the shadow price of the population's suering on a level above the total logistics costs, which could lead decision-makers towards prioritizing deprivation. At the same time, the highest possible deprivation value is only about 4% above the lowest value, indicating that the decision does not strongly inuence deprivation in absolute terms. On the other hand, the highest logistics costs are almost three times higher than the lowest, indicating that the decision strongly aects logistics costs. Moreover, logistics costs would have negligible eects in the location selection process without the normalization. Since the decision mostly aects logistics costs, normalization could lead decision-makers towards prioritizing logistics costs.

The results highlight the extremely challenging task decision-makers face in disasters and the necessity to support them in making their decision. While the developed approach can increase transparency and suggest locations, the decision and consequences need to be accepted by as many people as possible. Therefore, a broad public debate should be striven for.

Furthermore, the approach still provides opportunities for further improvements and adaptations. While it allows making decisions without dening a new DCF that ts the local economic context, it still requires a DCF or DLF as an input parameter. Even though the concept of deprivation is widely accepted, there is still only a limited number of functions available (Shao et al. 2020), inhibiting the application in case of a demand for extraordinary goods. Moreover, the dependency on the preferences of the decision-maker increases the risk of power abuse. Therefore, it is essential to assess the whole set of options and discuss the implications of selections with multiple stakeholders. Despite these limitations, the case study highlighted possibilities to deepen the understanding of the consequences of disaster relief decisions. Consequently, the new approach can contribute to better decisions in future disasters and support decision-makers eectively.

# **4.4. Study D: Selecting Locations for Mobile Water Distribution Systems**

The following section refers to the content of the article "On the combination of water emergency wells and mobile treatment systems: a case study of the city of Berlin." This article was written in collaboration with Christoph Stallkamp, Markus Lüttenberg, Marcus Wiens, Rebekka Volk, and Frank Schultmann, and was published in the journal Annals of Operations Research as Stallkamp et al. (2020).

### **Study context and contributions**

The study's objective is to develop a tool to support decision-makers in selecting locations for mobile water purication systems.

The German disaster management authorities have around 4.800 gridindependent emergency wells at their disposal (BBK 2008). These wells can

be activated in case of contamination in the water supply system, e.g., caused by a terrorist attack. Beneciaries visit the wells, pick up water for their immediate demand, and carry the water back home. Consequently, the distance between the beneciaries and wells should be as short as possible. Since the wells' locations were selected and built before Germany got reunited, they do not reect the demographic properly. This applies especially in areas of the former GDR (BBK 2008), leading to large distances between beneciaries and wells. Therefore, an intervention based on wells alone seems to be not sophisticated enough to account for the population's suering.

While dierent technologies could be facilitated to increase the supply of water further (e.g., household-based ceramic water lters (Clasen and Boisson 2006)), mobile water treatment systems (WTS) oer the chance to start a large scale intervention fast and with a limited number of required components.

Figure 4.6 provides an overview on the study's components. While module A aims to determine the water demand, the focus of module B is to provide an overview of the collection and pre-processing steps for wells and WTS. Moreover, module C contains the development of a mixed-integer linear optimization problem to select locations, together with various possibilities to better understand the trade-os of decisions and, therefore, oer decision support to authorities.

Figure 4.6: Overview of Study D's model requirements and modules with the chosen modeling approaches (modules A, B, C; see also Figure D.1).

From an academic perspective, the study oers three main contributions to the body of literature. First, the introduced structure can be followed when determining the capacities of an existing well system. Second, a maximum covering model to support the process of location selection for WTS is introduced. Third, the comparison of dierent trade-os allows decision-makers to deepen their understanding of relief interventions more thoroughly and, thus, can improve both preparedness for disasters and the response after future disasters.

The application of the model to a case study highlights the power of the approach. Within the case study, the response to hypothetical water contamination in the city of Berlin is analyzed. The German Federal Agency for Technical Relief (THW) owns 14 large-scale UF-15 WTS that are considered potential water sources complementing Berlin's wells. These UF-15 systems can purify up to 300,000 liters of water per day to drinking water quality. A presentation of the main results and points of discussion of the study follows.

### **Results and Discussion**

The case study highlights the importance of extending emergency water supply beyond the limits of emergency wells. Under the selected assumptions, emergency wells can only oer water to up to 77% of the population's demand. On the other hand, this number can increase to up to 84% if WTS complement the wells. However, the selected locations dier strongly if decision-makers prioritize parts of the population – e.g., vulnerable groups such as people in hospitals or nursing homes. The maximization of total supply leads to WTS-locations that are mostly located on the outskirts of Berlin. On the other hand, a lexicographic maximization approach, in which vulnerable groups are prioritized over the general population's demand, suggests installing WTS at more central locations. Figure 4.7 highlights the selected locations. Furthermore, trade-os regarding the maximum length of the supply route, the required number of WTS to fulll a certain coverage level, and between cost and coverage are discussed in the published article.

The study eciently highlights the importance of improving the emergency water distribution system and extending the available resources. Even though the currently available WTS increase the attainable degree of coverage, it is not possible to supply water to 16% of the population.

Moreover, the study sheds light on the trade-os decision-makers have to consider when facing disaster relief decisions. Even though these decision-makers are usually experienced logisticians, elected politicians, or both, having expert knowledge in their eld, decisions like prioritizing parts of the population or investing in specic technologies come with drastic consequences for society. Therefore, it is necessary to provide people in charge with as much support as possible. This could be done both with studies like the present one, complemented with public debates. For example, one component of the study was to understand the eects of an increased number of WTS and to analyze the eects of a more cost-ecient deployment of expenditures (in favor of other strategies to use available money). Therefore, considering debates, the general society needs to start forming a consent about the degree to which vulnerable people should be prioritized or if it is possible to determine a limit of tolerable consequences for the general population. Similar debates also inuenced decisions during the COVID-19 pandemic response, e.g., regarding tolerable contact restrictions during the Christmas holidays or the tolerable economic burden due to the large nancial aid volume.

Consequently, the developed approach increases transparency signicantly and provides decision-makers with valuable information.

# **4.5. Study E: The Value of Accurate Demand Information in PPECs**

The following section refers to the content of the article "On the Value of Accurate Demand Information in Public-Private Emergency Collaborations." This article was written in collaboration with Miriam Klein, Marcus Wiens, Markus Lüttenberg, and Frank Schultmann, and was published in the KIT-IIP Working Paper Series in Production and Energy as Diehlmann, Klein, et al. (2020).

### **Study context and contributions**

The study's objective is to introduce a framework to quantify the value of accurate information and the planning error due to incorrect information in disaster relief.

Private actors supply goods to the population during ordinary times and interact with their customers regularly. Therefore, they understand the market better than authorities do, which are mainly during disaster relief. Consequently, authorities are motivated to collect more accurate information. For example, this can be done by purchasing market data (similar to the US FEMA, which recently spent 3.6 million USD on market information (Federaltimes 2019)) or, more drastically, by a state-ordered release of demand information during a disaster. However, these measures represent intense market interference and should, therefore, be assessed carefully in advance. To support this assessment, a framework to quantify the value of this information was developed and introduced in the study.

Figure 4.8 highlights the framework. Assuming a demand spike in the aftermath of a disaster, supermarket capacities are exceeded, leaving parts of the population without supplies. Consequently, authorities try to distribute essential goods to the people who cannot be supplied by the companies anymore. This is achieved by opening or remodeling facilities for the distribution, e.g., schools.

While authorities only have access to general demographic information, they cannot distinguish between dierent characteristics of demand points (DPs) representing beneciaries. Therefore, they need to treat every DP equally (grey boxes). On the other hand, private actors understand dierent demand patterns better. If, for example, people living on a block did not increase their water purchases during a minor water contamination a couple of months ago, they most likely will have water stored at home. Therefore, their water purchases will most likely not increase again, leading to a more specic assessment of the same people's demand (green boxes).

Including such information into authorities' school selection decisions leads to dierent outcomes (ED-Scenario (Equal Demand) and I-Scenario (Information)). Moreover, the combination of the locations selected with inaccurate information and the real demand points leads to the so-called CO-Scenario (Combined Outcome), which can be interpreted as the realization of a plan based on inaccurate information. Consequently, the dierence between CO-Scenario and ED-Scenario (in terms of a selected key performance indicator) represents the planning error. Furthermore, the dierence between I-Scenario and CO-Scenario represents the value of accurate information. Moreover, the frameworks' exible set-up allows decision-makers to analyze multiple addi-

tional situations and, therefore, oers a valuable contribution for planners, decision-makers, and researchers.

The framework is applied to a case study of hypothetical water contamination in the city of Berlin. Assuming a short time spike of water demand, authorities need to select schools as temporary water distribution points. In the present case study, these schools' selection is conducted with a mixed-integer linear program maximizing the delivered water. Following the framework described above, authorities can only plan with publicly available census data. Simultaneously, supermarkets have more detailed data available.2 A presentation of the main results and points of discussion of the study follows.

### **Results and Discussion**

Figure 4.9 highlights the selected schools to open within the three scenarios (ED, CO, and I) in case authorities have the capabilities to open 33 schools.3

In the ED-Scenario, the mixed-integer linear program selected schools mostly located in Berlin's center. However, merging the actual demand distribution with these locations indicates that the locations are not selected adequately (CO-Scenario). Consequently, 23% fewer goods than planned can be distributed, representing the planning error. On the other hand, total supply increases by 19% in case authorities base their plan on accurate demand, representing the value of information (I-Scenario).

Furthermore, the framework allows for analyzing two extensions. The rst extension aims towards a better understanding of the intervention's starting time. While authorities started operating their school-based distribution after supermarkets had run out of goods in the basic version of the framework, extension I regards simultaneously operated schools and supermarkets. Furthermore, the second extension aims to better understand the competition between supermarkets and school-based distribution centers. In the basic version of the framework, school locations were selected considering the demand that cannot be covered by the supermarkets. In extension II, authorities regard the whole population's demand and operate the schools simultaneously

2 The study's market data was simulated due to condentiality reasons.

3 The sum of the capacity of 33 schools and Berlin's supermarkets roughly equals the total demand.

to the supermarkets. Consequently, crowding-out eects are more likely to occur.

Figure 4.10 presents the amount of water distributed via the dierent distribution channels for the basic version of the framework and the two extensions. In contrast to the intuitively expected result, crowding-out eects are weaker in the case of a more intense market intervention. By selecting schools that maximize the total supply through supermarkets and schools, authorities implicitly consider the maximization of supermarkets' supply into their optimization models. Consequently, supermarkets benet from stronger interventions and are recommended to collaborate with authorities if they expect an intervention. If, for example, authorities reached out and asked for information on areas that are expected to become undersupplied by the private sector, they could consider sharing additional information on the whole supply. However, it has to be noted that crowding-out cannot be avoided. Consequently, supermarkets still need compensation for the intervention.

Figure 4.10: Overview of the amount of water distributed via schools, supermarkets, and total within the dierent model extensions.

The study oers multiple directions for future research. First, the assumption of a static disaster context might not hold in reality. Examples of potentially dynamic parameters are demand and supply, which might increase fast or unfold slowly. Consequently, multi-period approaches might shed light on ways to optimize the order to open schools or the importance of anticipating

demand information in disasters. Second, the study assumes equal treatment for every beneciary. As shown in Stallkamp et al. (2020), the prioritization of vulnerable parts of the population might be necessary, though it can signicantly reduce total supply. Considering dierent demand types might oer the chance to distinguish the value of information for dierent types of demand information, leading to a deeper understanding of prioritization consequences. Third, the operationalization of the collaboration requires further preparation. If, for example, supermarket chains were to share information with authorities, questions on the data format, requirements for IT-interfaces, data protection issues, and many more would arise and take much time to agree on. Consequently, disaster response exercises could help identify such problems and handle them prior to the disaster, leading to better-dened processes and, thus, an increased speed of the disaster intervention.

# **5. Managerial Implications**

After summarizing the studies' results, the managerial implementations for the four cross-study topics introduced in chapter 2.4 – Information Management, Company Involvement, German Specities, and Trade-os – follow.

# **5.1. Collect Information Before the Start of the Disaster**

Studies A, B, D, and E provided valuable insights into the importance of information in disasters. Study A identied asymmetric information as a challenge for collaboration in disaster relief. Study B stressed how information is interpreted dierently by the respective relief actors. Furthermore, Study D underlined that decisions might change by including precise information regarding, e.g., a person's vulnerability. Finally, Study E shed light on the importance of accurate information and highlighted the complexity of approximating demand information from publicly available resources.

The diversity of potentially helpful information indicates that a variety of actors could contribute important information. However, the majority of relevant datasets are critical from a business condentiality or data protection perspective. Consequently, these actors either need compensation for sharing data or guarantees that sharing data will not negatively aect their organization. In case they agree to share data, issues such as data format, granularity, or the selection of an exchange platform may arise.

Creative solutions can facilitate collaboration and information exchange. In this context, blockchain technology might be a promising option since it "provides an integrity protected data storage and allows to provide process transparency" (Wust and Gervais 2018, p. 45). Therefore, blockchains could work as a single platform of communication in disasters, connecting donors and transportation companies or ensuring the verication of the authenticity of relief goods (SCB 2019). Other application concepts include using blockchains for victim identication via sensitive dental data (AlQahtani et al. 2019) or coordinating the information exchange between ground vehicles and unmanned aerial vehicles scanning disaster areas (Su et al. 2020).

Nevertheless, it becomes evident that the information required for an ecient relief decision is too complex to be collected without preparation in the aftermath of a disaster. Decision-makers must address ways to obtain a comprehensive set of information before the disaster started. In addition to complex state-of-the-art technologies, traditional (open-source) databases might already provide sucient information for dedicated decision support models. For example, the city of Berlin publicly shares georeferenced data (Berlin 2019). This data can be further used in studies addressing dierent disaster components, increasing the understanding of local characteristics for disaster response. The fact that most of the companion articles for the present dissertation were conducted using Berlin's publicly available data underlines this further.

# **5.2. Organize Training Exercises With Companies**

Studies A, B, C, and E addressed the involvement of companies in disaster relief. Study A oered multiple examples of PPECs, as well as guidelines for modeling these collaborations. Study B underlined the challenges of identifying options that both actors benet from, while Study C shed light on selecting commercial buildings as temporary distribution centers. Moreover, Study E emphasized the potential eects of authorities' interventions on the private sector.

Together with the current developments in the COVID-19 pandemic, the results of the studies highlight that companies are already heavily involved in relief logistics. Despite authorities' formal responsibility during disasters, companies signicantly reduce the aected population's suering by keeping their distribution channels open (for commercial and altruistic reasons). Thus, increasing the private sector's resilience reduces the burden on authorities. Moreover, authorities implicitly rely on companies' resources – for example, transportation capacities, buildings, or goods. Therefore, joint exercises are highly recommended to improve interoperability by ensuring that access to

and operation of the potentially required resources, as well as the related communication processes, work properly. For instance, this could be addressed in one of the upcoming LÜKEX1 campaigns, in which authorities practice collaboration between dierent ministries and bureaus during a large scale disaster intervention. Standardization and practicing of processes can speed up the disaster intervention and provide necessary relief.

# **5.3. Adjust the German Disaster Management System**

Dierent aspects of the German disaster management system were analyzed in Studies B, D, and E. Study B highlighted the potential of closer collaboration of public and private actors during the storage of goods that might be demanded in a disaster. Study D addressed available infrastructure for water supply and Study E Berlin's local school network as potential PoDs.

The studies hint towards slightly outdated structures that require modernization. For example, Study B highlighted that it is not possible to supply Berlin's population in a disaster with wells alone. Even though the emergency wells are maintained and new wells built from time to time, it is highly unlikely that the well system is sucient during disaster relief, considering that almost 20% of Germany's emergency wells are located in Berlin (Fischer and Wienand 2013). Moreover, maintaining and extending the well system requires high nancial and organizational eort. On the other hand, during one of the NOLAN-workshops, experts stressed that emergency wells only serve as the "last resort," and other means of supply might be helpful during small-scale disruptions. Consequently, further investments in mobile treatment systems or other exible solutions might be reasonable options for decision-makers that want to increase water supply resilience in disasters.

Another essential aspect to consider, which is closely connected to the issue of Information Management, is the high degree of coordination eort. Authorities need to activate and communicate with diverse public ministries, bureaus, and organizations, which will slow down response speed (especially

1 "Interministerial and Interstate Crisis Management Exercise," in German: "Länder- und Ressortübergreifende Krisenmanagementübung (Exercise)."

if legal factors and procedures have to be followed in the context of Amtshilfe). Furthermore, informing beneciaries about the relevant details of authorities' operational plans is challenging – for example, the location of the allocated PoD. Moreover, the COVID-19 pandemic and the development of the German Corona-Warn-App stressed the high priority of data protection in Germany (DW 2020c). Consequently, creative solutions are required to prevent bureaucracy and data protection from slowing down the response speed. These creative solutions could include blockchain technologies and the transfer of the recently developed Corona-Warn-App to other application options. An example could be anonymously tracking the frequency a PoD is visited via the application's interfaces, as well as using the application to distribute food stamps, or collect voluntarily shared information in a crowdsourcing initiative.

# **5.4. Initiate Public Debates About Prioritization During Disaster Relief**

Studies A, B, C, and D highlighted the importance of understanding tradeos in disaster relief. Study A discussed the dierent objectives in disaster collaborations, while an approach to identify potential compromises in location selection was introduced in Study B. Moreover, Study C focused on the trade-o between human suering and nancial aspects during a relief intervention, while Study D addressed the trade-os that come along with prioritizing vulnerable parts of the population.

It is almost impossible to make relief decisions without (explicitly or implicitly) considering trade-os. Financial trade-os have been highly debated in commercial literature and attracted some attention in relief logistics in the context of deprivation costs. However, the implications of non-monetary trade-os should be further investigated. For example, sustainability topics, such as minimizing waste or emissions while maximizing supply, could also become important for decision-makers. Moreover, trade-os that arise by prioritizing one part of the population over another need to be investigated further. It might be reasonable to prioritize vulnerable groups (for example, people in special care facilities) from a philosophical or moral perspective. On the other hand, Study D highlighted the drastic consequences for the nonprioritized groups. Consequently, there should be a public debate about if, how, and at which cost authorities should prioritize parts of the population.

An example of such a discussion is the COVID-19 vaccine distribution debate. A group of experts from various German organizations2 opened the debate by suggesting a general vaccination-prioritization strategy for the German population (Ethikrat 2020). Afterward, this strategy was open for debate long before the vaccine distribution was expected to start. Similar debates on critical issues, such as: "who should receive water in case there is not enough water?" or "who should get access to critical goods rst if there is hypothetically enough for everyone, but everyone needs them at the same time?" could therefore be initiated by authorities. However, starting these kinds of debates will be challenging and needs to be prepared thoroughly to avoid the risk of a debate based on panic and fears. Nevertheless, besides probably increasing the acceptance of these measures during an upcoming disaster, transparently communicating a strategy agreed upon by the majority of the population might lead to better disaster preparation of non-prioritized groups and, thus, less suering of the whole population.

2 The German Permanent Vaccination Commission, German Ethics Council, and the German National Academy of Sciences Leopoldina.

# **6. Conclusion**

Relief logistics aims to deliver goods to beneciaries in the aftermath of a disaster. During this process, three dierent types of facilities are essential: warehouses, distribution centers, and distribution points. The selected locations of facilities signicantly aect the outcome of disaster response. Therefore, decision-support models improving the process of location selection increase resilience towards disasters.

The dissertation contributes to the literature with ve studies, increasing the understanding of disaster relief logistics and supporting decision-makers during dierent facility location decisions. Thus, it provides valuable insights into making relief interventions more ecient. Study A stresses the importance of addressing dierences in resources, capabilities, and strategies in PPECs. Moreover, power dierences, information sharing, and partner selection are essential to consider. Study B indicates that, even though both types of actors follow dierent strategies and goals, it is possible to identify candidate locations with the help of a new approach based on multi-objective mixed-integer optimization models. Study C highlights that decision-makers could better understand the implications of their decisions with the help of a normalized weighted sum approach. Study D indicates that a combination of wells and mobile systems could improve humanitarian water supply chains in disasters. Selected locations change signicantly if vulnerable parts of the population are prioritized. Study E introduces a framework that allows determining the value of information and the planning error due to inaccurate information. Consequently, the studies contribute to an increase in the eciency of the relief system.

Furthermore, implications for four cross-study topics – Information Management, Company Involvement, German Specities, and Trade-os – were derived. Information should be collected before a disaster stroke since the complexity of collecting and exchanging information will signicantly slow down relief eorts otherwise. Furthermore, the need to exercise with companies became obvious since authorities already strongly rely on company

resources. Moreover, adjustments to the German disaster management system are recommended. This includes, inter alia, investments into mobile water treatment systems or creative solutions for more ecient coordination during disasters. Finally, a public debate on the consequences of implicitly or explicitly prioritizing parts of the population has the potential to increase the public acceptance of a prioritization strategy and, thereby, reduce the burden on authorities signicantly. Initiating research projects preparing these debates could be a rst step to approach this issue.

Future studies aiming to improve location planning further may, in addition to the opportunities mentioned in chapter 4, address these cross-study topics. For example, developing a data exchange interface can increase the available input data for location optimization models. Guidelines to standardize PPECs and a toolbox for exercises can ensure ecient implementation of plans in case of a disaster. A holistic analysis of the German disaster warehouses' location under consideration of the private sector might reduce authorities' warehousing costs. Similarly, a shift from stockpiling grains towards storing ready-to-eat meals can reduce the complexity of the distribution, bypassing the preprocessing steps currently required before distributing food to the population. However, the accompanying adaptations to the warehouse system need to be studied thoroughly rst. Moreover, future studies could aim to improve the communication of location decisions. For example, a disaster dashboard highlighting selected PoDs could ensure that prioritized beneciaries receive their goods. The implementation into an already existing mobile application might facilitate this process.

Despite the opportunities to improve disaster relief further, statistics indicate that, from a global perspective, disaster management is developing in a promising direction. An example of such a promising development is the ongoing century-wide decline of average deaths per year caused by disasters while the total number of disasters increases (Forbes 2020). The COVID-19 pandemic is likely to stop this trend and revealed that there is still much room for improvements. Moreover, the increasing probability of disasters like zoonoses caused by the eects of Globalization (Gibb et al. 2020) or an increasing number of hurricanes caused by Global Warming (Webster et al. 2005) indicates that the number of disasters is likely to keep increasing in the future. Nevertheless, the promising long-term trend highlights that disaster management continuously improved over the last century, allowing disaster managers to face the upcoming challenges with optimism. The present dissertation can contribute to this positive long-term development, improve the

response towards upcoming disasters and, therefore, reduce the suering of aected people in the future.

# **7. Bibliography**

Wust, Karl and Arthur Gervais (2018). "Do you Need a Blockchain?" In: 2018 Crypto Valley Conference on Blockchain Technology. CVCBT 2018 : 20-22 June 2018, Zug, Switzerland : proceedings. 2018 Crypto Valley Conference on Blockchain Technology (CVCBT) (Zug). Ed. by Emin Gün Sirer, Arthur Gervais, and Alexander Denzler. Crypto Valley Conference on Blockchain Technology et al. Piscataway, NJ: IEEE, pp. 45–54.

# **Part II.**

# **Companion Articles**

# **Overview of Related Publications**

# Study A

Diehlmann, F.; Lüttenberg, M.; Verdonck, L.; Wiens, M.; Zienau, A.; Schultmann, F. (2020). Public-Private Collaborations in Emergency Logistics: A Framework based on Logistical and Game-Theoretical Concepts. KIT-IIP Working Paper Series in Production and Energy.

### Study B

Löel, M.1 ; Diehlmann, F.1 ; Zienau, A.; Lüttenberg, M.; Wiens, M.; Wagner, S. M.; Schultmann, F. (2020). Identifying Joint Warehouse Locations in Public-Private Emergency Collaborations. Submitted to a Scientic Journal.

# Study C

Diehlmann, F.; Hiemsch, P. S.; Wiens, M.; Lüttenberg, M.; Schultmann, F. (2020). A Novel Approach to Include Social Costs in Humanitarian Objective Functions. KIT-IIP Working Paper Series in Production and Energy.

# Study D

Stallkamp, C.1 ; Diehlmann, F.1 ; Lüttenberg, M.; Wiens, M.; Volk, R.; Schultmann, F. (2020). On the combination of water emergency wells and mobile treatment systems: a case study of the city of Berlin. Annals of Operations Research [in Press].

# Study E

Diehlmann, F.; Klein, M.; Wiens, M.; Lüttenberg, M.; Schultmann, F. (2020). On the Value of Accurate Demand Information in Public-Private Emergency Collaborations. KIT-IIP Working Paper Series in Production and Energy.

1 These authors contributed equally to the manuscript.

# **A. Public-Private Collaborations in Emergency Logistics: A Framework Based on Logistical and Game-Theoretical Concepts**

# **Abstract1**

Collaboration in emergency logistics can be benecial for governmental actors when supply chains need to be set up immediately. In comparison to research on humanitarian-business partnerships, the body of literature on so-called Public-Private Emergency Collaborations (PPEC) remains scarce. Private companies are only rarely considered within research on emergency collaborations, although they could contribute to a more ecient supply of goods given their resources and existing communication networks. Based on this research gap, this paper develops a logistical and game-theoretical modeling framework for public-private emergency collaborations. We characterize both public and private actors' possible roles in emergency logistics based on literature research and real cases. Furthermore, we provide an overview on existing PPECs and the challenges they are confronted with. The concluding framework contains aspects from humanitarian logistics on the governmental side and from business continuity management (BCM) or corporate social responsibility (CSR) on the commercial side. To address the challenge of evaluating dierent objectives in a collaboration, we add a game-theoretical approach to highlight the incentive structure of both parties

1 This chapter includes the preprint of the article "Public-Private Collaborations in Emergency Logistics: A Framework based on Logistical and Game-Theoretical Concepts" by Markus Lüttenberg, Lotte Verdonck, Marcus Wiens, Alexander Zienau, Frank Schultmann, and myself (Diehlmann, Lüttenberg, et al. 2020).

in such a collaboration. In this way, we contribute to the research eld by quantitatively evaluating public-private collaboration in emergency logistics while considering the problem-specic challenge of the parties' dierent objectives.

# **A.1. Introduction and motivation**

In 2018, earthquakes and tsunamis resulted in the loss of 10,733 lives, while extreme weather led to 61.7 million people aected by natural hazards (UN-DRR 2019). According to Worldbank (2019), global losses caused by natural hazards have quadrupled from \$50 billion a year in the 1980s to \$200 billion in the last decade. Moreover, population growth and increased urbanization lead to rising disaster impacts (Worldbank 2019).

van Wassenhove (2006) highlights that around 80% of all relief eorts after disasters are related to logistics. Consequently, all involved actors need to establish well dened relief logistics procedures to protect the aected population. While emergency management focuses on the management of all actions directly after the impact of a disaster (see for instance Tatham and Spens (2011)), the term "emergency logistics" can be dened as "a process of planning, managing and controlling the ecient ows of relief, information, and services from the points of origin to the points of destination to meet the urgent needs of the aected people under emergency conditions" (Sheu 2007).

Within the limits of the concrete disaster scenario, companies can still dispose over most of their capabilities to respond to the disaster, while established supply chain structures are severely interrupted during catastrophes (Holguín-Veras, Jaller, et al. 2012). Higher resilience provided by public and private actors, cooperatively involved in disaster relief, can therefore help to prevent the shift from a critical or disastrous situation to a catastrophic disaster, resulting in a reduction of the burden on the population and companies.

The focus of this paper is to describe and model the scope and potential of emergency collaboration between private rms on the one hand and the government on the other, hence a Public-Private Emergency Collaboration (PPEC). Although researchers agree that multiple actors play an important role in relief logistics (Balcik et al. 2010; Kapucu, Arslan, and Demiroz 2010; Kovács and Spens 2007), real world cases that develop quantitative disaster relief models for civil protection agencies and other governmental authorities are rarely considered in the literature. One reason for this phenomenon could be that - compared to governmental agencies - humanitarian organizations are more willing to provide researchers with data that they are allowed to publish (and/or funding) in exchange for scientic knowledge and experience (Arnette and Zobel 2019; Duran, Gutierrez, and Keskinocak 2011; Gatignon, van Wassenhove, and Charles 2010; Pedraza-Martinez and Van Wassenhove 2013; Saputra et al. 2015; Laan et al. 2016). In contrast, data received in cooperation with public authorities and governments often contains critical knowledge that researchers might not be allowed to share publicly (Goolsby 2005). However, an exclusive research focus on non-prot humanitarian organizations in the quantitative relief management context might lead to a trend to analyze ways to ght the symptoms instead of tackling the roots of the problem. It can be argued that the role of non-prot humanitarian organizations in humanitarian logistics primarily exists due to a lack of resilience in the market or in the public disaster management system.

Figure A.1: Classication of phases or activities for dierent types of actors during a crisis.

From a conceptional point of view, activities of actors after a disaster can be classied as in Figure A.1 (note that real cases may vary from this - for instance due to very strong and active NGOs or comparably ineective public or private actors). Firms deal with uctuations in demand or supply as well as with disruptions in their supply chain in the context of their Business Continuity Management (BCM) on a regular basis (see for instance Schätter et al. (2019)). Their reactions focus on getting back to "business as usual" as soon as possible (Palin 2017; Macdonald and Corsi 2013). Once a disruption in supply impacts the population or critical infrastructures signicantly, the state needs to become active to ensure the population's well being (Wiens et al. (2018), "I" in Figure A.1). These operations can be signicantly improved by a collaboration with private rms (PPEC). The importance of the private sector is underlined by Izumi and Shaw (2015), who point out that 70-85% of investments in emergency logistics are expected to come from the private sector.

While humanitarian organizations (HOs) can operate humanitarian supply chains without the occurrence of a disaster, they sometimes play an important role in emergency logistics as well. Their activity usually starts once the impact of the disaster reaches another critical threshold - for instance, because they get signicantly more donations if the crisis receives more attention by the media due to increased severity, or due to the time it takes to collect donations (II). In this phase, all actors ght the situation at the same time and need to directly or indirectly work together to ensure ecient relief management (Catastrophe Collaboration). Once the disaster becomes less severe or the HOs run out of funding, HOs leave the area again (III). Finally, the private sector takes over and processes normalize again once the state stops its intervention (IV). Moreover, it has to be noted that in extremely severe situations, NGOs might become active right away (V) or stay active until the market takes over again (VI).

Accounting for these phases, improved emergency management procedures within the private and the public sector can reduce the burden on the population signicantly (Papadopoulos et al. 2017). Therefore, it prevents the worsening of the situation and that the disaster turns into a catastrophe. One way to achieve improvement is to establish sustainable collaboration mechanisms, since collaboration signicantly improves eciency and eectiveness of emergency response activities (Balcik et al. 2010; Kapucu, Arslan, and Demiroz 2010). However, in spite of the prominent opinion stressing the importance of multiple actors in crisis management, most of the studies in the eld of humanitarian supply chain management focus on a single actor (Behl and Dutta 2019). In our view, sustainable and – from a welfare perspective – ecient crisis management research primarily requires in-depth research on the way private rms and public organizations deal with emergencies together. While collaboration increases the eciency of the logistical operations, incentives and a surplus for all involved partners are critical as well. Consequently, a comprehensive account on collaboration in emergency logistics operations

requires a profound understanding of both, the operational logistics perspective on the one hand and the incentive-oriented game-theoretic perspective on the other.

However, in comparison to research on humanitarian-business partnerships (Fikar, Gronalt, and Hirsch 2016; Nurmala, Vries, and Leeuw 2018; Tomasini and Van Wassenhove 2009), the body of literature on PPECs remains scarce (Chen et al. 2013; Gabler, Richey, and Stewart 2017; Stewart, Kolluru, and M. Smith 2009; Swanson and R. J. Smith 2013; Wang, Wu, et al. 2016; Wiens et al. 2018). Moreover, to the best of our knowledge, only two publications exist that explicitly consider logistical and game-theoretical approaches in the disaster context simultaneously (Nagurney, Flores, and Soylu 2016; Nagurney, Salarpour, and Daniele 2019). Even though the authors analyzed competition and collaboration of humanitarian organizations, they did not regard the collaboration of public and private actors in disaster management. This paper aims to ll this research gap.

The main contribution of this paper can be summarized as follows. A framework for public-private emergency collaborations is developed based on logistical and game-theoretical concepts. On the one hand, the operations research perspective on PPECs is highlighted by describing the requirements, characteristics, and challenges for logistical PPEC-models. On the other hand, game-theoretical questions are considered regarding contract design and the requirements for collaboration that are mandatory to ensure stable and ecient relationships. In this way, we contribute to the research eld by quantitatively modeling public-private collaboration in emergency logistics while considering the problem-specic challenge of the parties' dierent objectives.

The remainder of this paper is organized as follows. Section 2 discusses the concept of PPECs. Following, we analyze the role of public and private actors involved in emergency logistics and address relevant characteristics of a PPEC from both perspectives. An overview on logistical challenges that need to be regarded in PPEC models follows in Section 4. We complete the modeling framework by considering game-theoretical aspects of a PPEC and providing an illustrative game-theoretical example in Section 5. Section 6 draws conclusions from our ndings.

# **A.2. Public-private emergency collaborations**

The concept of a PPEC is closely related to the well established concept of a Public-Private Partnership (PPP). Therefore, we rst provide an overview on PPPs in general and build the bridge to PPECs in crisis management, which are confronted with specic challenges but also entail high potential for improvement of crisis operations. We discuss the potentials and limits of a PPEC from a wider economic perspective and focus on the incentives of the collaborating partners. Following, we present dierent forms of already established PPECs. In line with the denitions provided by Wankmüller and Reiner (2020), the term "collaboration" is preferred in the PPEC context as a collaboration aims to establish a close, intense and long-term relationship between organizations to solve problems jointly. On the contrary, "cooperation" is a short-term phenomenon, which primarily relates to partnerships established in the preparedness and immediate response phases to disasters (Schulz and Blecken 2010).

### **A.2.1. Public-private partnerships in general**

There is no ocial denition of public-private partnerships (PPPs) available in literature (Worldbank 2018). However, PPPs follow the general principle that the collaboration of the public sector with the private sector leads to (1) eciency gains and (2) an optimal distribution of the risk (Iossa and Martimort 2015). PPPs ensure the involvement of private partners with both the expertise and the nancial resources that may not be readily available in the public sector (Swanson and R. J. Smith 2013). The concept of PPPs was rst established in the infrastructure sector (Delmon 2011) and the transportation sector (Grimsey and Lewis 2004). Nowadays, they are also applied to social projects (Fandel, Giese, and Mohn 2012), in the healthcare sector, for schooling projects, or in waste management (Spoann et al. 2019). Saussier and Brux (2018) provide an overview on the current status of PPPs in theory and practice.

Several characteristics described in the literature are typical for PPP projects. First, PPP projects are aimed to last for a long-term period (Iossa and Saussier 2018), typically at least for 20 years. Second, PPP projects may be divided into dierent organizational parts - the building part, the operating part and the nancing part (Morasch and Toth 2008). Morasch and Toth (2008) argue that

the building part is usually executed by private rms, while the nancing part belongs to the power of the public sector. The operating part may vary in responsibility. Furthermore, the authors emphasize that in comparison with conventional procurement, where the public sector invites tenders for orders and the whole project is divided into several minor parts that are conducted from dierent rms, in PPP projects, tasks are bundled and under the responsibility of a single rm. As such, the degree of bundling is higher in PPP projects. Third, in comparison with a conventional project, the cost of a PPP project can exceed or undercut (Iossa and Martimort 2015). Iossa and Martimort (2015) further elaborate that an important cost-driver of PPPs are transaction costs which are almost uncorrelated with the total PPP volume. High transaction costs arise due to complexity of projects and contractual relationships (Carbonara, Costantino, and Pellegrino 2016). Therefore, Iossa and Martimort (2015) suggest that only high volume projects are relevant for consideration of a possible PPP contract. Fourth, Iossa and Martimort (2015) provide an overview on quality factors which need to be considered in PPP projects. They emphasize that every evaluation needs to be performed on a case by case basis, that the quality of the products and services that are part of the PPP contract needs to be analyzed, and that the quality is adequately specied.

To summarize this section, major factors under consideration for the evaluation of a PPP are (1) the period of time the project is forecasted to last, (2) what parts of the projects are privatized and which remain under the control of the public counterpart, (3) the complexity of the contractual design together with the resulting transaction costs (Osei-Kyei and Chan 2015) and, (4) the quality factors of the project itself.

### **A.2.2. PPEC barriers, requirements and potential benefits**

In general, PPECs should be consistent with the ten "Guiding Principles for Public-Private Collaboration for Humanitarian Action" acknowledged by the World Economic Forum and UN-OCHA (World Economic Forum (WEF) and UN-OCHA 2008). The idea is that partnerships with rms facilitate the transfer of knowledge and skills on collaborative logistics and supply chain management, leading to eciency gains in humanitarian logistics (Nurmala, Leeuw, and Wout Dullaert 2017). Moreover, PPECs may help to create more

resilient infrastructure systems, thereby helping to improve the situation of the population (Boyer 2019).

However, several real-life examples highlight that the public sector struggles to collaborate with the private sector eciently. One case is Hurricane Katrina, in which the successful emergency response of retailers, including Walmart, diametrically opposed the insucient performance of government agencies (Horwitz 2009; Sobel and Leeson 2006). Exemplary was the private sector's fast delivery of necessary goods like food and clothes to the places where they were needed, while the trucks under control of the governmental organization FEMA experienced a lot of diculties organizing and distributing essential supplies (Horwitz 2009). Another well-discussed case is the earthquake and tsunami hitting Japan in 2011, where the government excluded private companies from the impact zone and attempted to create entirely new supply networks. As a result, millions of people with a real need for food could not reach commercial organizations, while those outside the disaster area started hoarding (Palin 2017). This raises the question why such collaborations between public and private actors did not succeed in the way they were supposed to. We argue that there is a signicant potential for collaboration but that this potential is more dicult to identify and "extract" compared to other forms of collaboration.

The motivation for both partners to participate in disaster management diers (Gabler, Richey, and Stewart 2017), and so do the required incentives. In the following paragraphs, we will briey outline the basic economic prerequisites for collaboration, especially from an incentive (or game-theoretic) perspective. In Section 4, we will discuss the options for collaboration in the eld of logistics and emergency logistics in more detail.

In economics, the agency theory (Milgrom and Roberts 1992; Townsend 1982), contract theory (Salanié 1997) and the theory of relational contracts (Gintis 2000; Macaulay 1963; Macleod 2006) form the methodological framework for the analysis of collaboration between actors with – at least partially – conicting objectives. In addition to the theoretical foundation, behavioral experimental economics contributed enormously to this eld of research over the last decades. Collaborative agreements can signicantly reduce transaction cost but have to cope with agency-specic risks based on asymmetries of power and information, such as exploitation, hold-up problems, or moral hazard (Fudenberg and Tirole 1991). Key factors for a stable and

ecient collaboration are (among many others) open (Jüttner 2005) and credible communication (Farrel and Rabin 1996) about the partners' objectives and intentions (Falk and Fischbacher 2006), transparent and fair allocation of risks and benets (Fehr and Gächter 2000) as well as the future perspective of an enduring relationship (Fudenberg and Maskin 1986). The possibility of a longer-term relationship allows the partners to stabilize their relationship on the basis of reciprocity and parallel expectations. From a game-theoretical point of view, relational contracts are self-enforcing contracts, since no external body (such as a court) is required to enforce the contractual interests, but the contract is fullled by mutual agreement and in the best self-interest. The range of application of these established concepts is broad and includes labor markets, project management, R&D collaboration and also public-private partnerships (Bing et al. 2005; Desrieux, Chong, and Saussier 2013).

In principle, most of these mechanisms can also be transferred to collaboration in crisis management (Solheim-Kile, Lædre, and Lohne 2019). However, there are a number of special features that should be emphasized because they could make (at least in part) collaboration more dicult if they are not adequately taken into account. First, in a PPEC the interests of the partners could be even more divergent than in classical infrastructure PPPs because the state's priority is on civil protection and on the provision of services of general interest. For companies, excessive investment in disaster prevention can result in competitive disadvantages. Second, this type of collaboration serves to prepare for a future event (disaster) that is only expected to occur with a relatively low probability. Large investments for this purpose must not only be economically justied, but also legally permissible.

However, there are private companies that directly participate in or support humanitarian operations with varying intensity and frequency (see section A.2.3 for a brief account on already established PPECs). Wiens et al. (2018) summarized the four major benets of a PPEC as follows: (1) Set up an early warning system based on real-time data, (2) allow information sharing between the partners and joint planning of evacuations, (3) avoid undesirable crowding out eects and (4) make use of the infrastructure, expertise and (technological) knowledge of the private sector. In addition to these collaborative benets, a PPEC can help to avoid costs and provide the requirements for a more ecient crisis management and an appropriate prioritization of tasks (Pettit, Fiksel, and Croxton 2010).

Additional advantages can result from an optimized division of tasks and improved coordination of logistics operations (see also Section A.4). As such, it can be concluded that a number of starting points for a public-private partnership in crisis management exist and that each of these aspects justies an in-depth model-based analysis.

### **A.2.3. Already established PPECs**

Even though the number of real-life cases is small, there are already a few existing examples of partnerships and networks which are (at least partly) structured as a public-private collaboration for crisis management. One example can be found in Sweden, where PPPs are implemented into the Swedish emergency preparedness management (Kaneberg 2018). Additionally, the US National Business Emergency Operations Center works as "FEMA's virtual clearing house for two-way information sharing between public and private sector stakeholders in preparing for, responding to, or recovering from disasters" (FEMA 2019). Participation works on a voluntary basis and is free of cost. Moreover, the German UP KRITIS - a public-private partnership focusing on critical infrastructures out of nine dierent sectors (e.g. water, nutrition, or energy) - has the goal to increase the resilience of these infrastructures and to fascilitate the exchange about current topics (UPKRITIS 2019).

These examples highlight the high potential of PPECs to increase eciency in emergency response. Furthermore, they show that the adequate management of involved actors is challenging and requires thorough preparation. While this list is by far not complete, it indicates the status of partnerships that have already been established and points to the diculties of taking into account the roles, interests and capabilities of the partners.

# **A.3. On the role of public and private actors in emergency management**

Kovács and Spens (2007) identify six types of actors in supply networks for humanitarian aid – donors, aid agencies, NGOs, governments, military, and logistics providers. Since these groups of actors pursue dierent (sub- )objectives and act under dierent conditions, uncoordinated intervention in

a crisis can quickly lead to an aggravation of the situation rather than to an improvement. Therefore, Balcik et al. (2010) highlight the need to collaborate and discuss challenges in the coordination, which are highly discussed in academic literature and which are the focus of Section A.4.

Although collaboration can happen on a voluntary, altruistic basis, the moral responsibility of private actors should not be neglected. For instance, Hesselman and Lane (2017) investigate roles and responsibilities of non-state actors during disaster relief from an international human rights perspective (inter alia, Article 25, which addresses food and shelter (United Nations 1948), connects PPECs with human rights issues). They conclude that non-public actors in disasters are indirectly obligated to become active, even though it might be dicult to hold them directly accountable. Therefore, Hesselman and Lane (2017) suggest that it could be one of the state's core task to include non-public actors into the disaster management processes using regulations. Within this context, it is necessary to understand the roles and tasks of the respective partners.

# **A.3.1. The role of public actors in emergency logistics**

In this paper, we dene "public actors" as all types of institutions and organizations under the control of public authorities on a federal and/or provincial level. This includes – inter alia – public disaster management institutions (for instance the US FEMA or the German THW), the military, police forces and reghters (as long as they are not privatized), and all types of ministries directly or indirectly involved in the relief process (legal, environmental, nancial etc.).

In general, the function of public actors in the domain of civil protection is to "provide security against unexpected threats that individual citizens cannot meet alone" (Comfort 2002). During emergency relief, they need to establish a safe environment for beneciaries and relief organizations. Moreover, public actors have critical resources at their disposal (Kovács and Spens 2007), which they use to support relief action physically (e.g. THW trucks) or nancially (e.g. through the FEMA Disaster Relief Fund). Furthermore, governments can ask foreign governments or HOs for support.

At the same time, "no international action can take place if the local government does not request it" (Day et al. 2012). In some cases, governments accept

foreign humanitarian work without supporting it actively (Akhtar, Marr, and Garnevska 2012) or even put up barriers to impede a HO's intervention (Kunz and Reiner 2016). Moreover, in very drastic cases, public actors can – if the legal context of the crisis area accounts for it – enforce the right to take possession over critical goods or resources (EIAS 2016). This can catch private actors by surprise and interfere with their planned processes signicantly. Due to legislative and moral responsibilities, public actors rst and foremost need to support the population during an emergency. This includes, for instance, to ght the reason of the crisis, to maintain public security, or to ensure that the population has access to essential goods.

The delivery of goods for a large amount of people requires a variety of resources (e.g. trucks, people). However, purchasing and maintaining resources is extremely costly – especially if the resources are only needed in extraordinary times. Consequently, public actors only have a comparably low number of resources at their direct disposal. Without a PPEC, public actors therefore need to hire logistics companies (for instance in the US via the Disaster Response Registry (SBA 2020)) or buy goods directly from private companies during a crisis. In developing countries, where the private sector is not as well equipped as in developed countries, the lack of resources therefore leads to, among others, the very prominent role of NGOs in crisis management. Regarding logistical challenges of a crisis, public actors can benet from a PPEC due to an increase in logistics capacities (Nurmala, Vries, and Leeuw 2018; Wang, Wu, et al. 2016) or access to logistical competences (Qiao, Nan, and Kang 2010; Tomasini and Van Wassenhove 2009).

At the same time, public actors provide special capabilities for a PPEC (see for instance Kovács and Tatham (2009)). First, public actors have specialized equipment and competences at their disposal. For instance, the German THW owns multiple mobile water purication plants (THW 2020). Military forces can provide necessary resources, communication devices, means of transport, medical services, water supply, and strong logistical and organizational structures (Carter 1992). Second, the government is legally empowered to enforce safety. They can do this with the help of police and/or military (Byrne 2013), or - in the case of a very strong escalation of a crisis – by adapting the laws (see for instance Halchin (2019)).

Furthermore, the involvement of private actors in the crisis management process can speed up the recovery process and help to let the market take over again faster (Palin 2017; Wiens et al. 2018). Strengthening these processes will help to increase the resilience of communities and supply chains (Chen et al. 2013; Mendoza, Lau, and Castillejos 2018; Pettit, Fiksel, and Croxton 2010).

### **A.3.2. The role of private actors in emergency logistics**

Emergency logistics becomes necessary if commercial supply chains are not capable to supply the population with sucient essential goods. This could be the case due to supply chain disruptions or a sudden increase in demand. When talking about private actors in the context of emergency logistics, we refer to those rms involved in the supply of essentials like food or medicine (e.g. producers, retailers, or logistics service providers).

These companies can contribute to emergency logistics with monetary donations, products, and services which can be provided in a commercial and non-commercial way (Hesselman and Lane 2017; Nurmala, Vries, and Leeuw 2018).

From a rm perspective, involvement in emergency logistics is an issue in BCM and CSR. BCM includes companies' planning and preparation of response and recovery to disruptions of business processes (Elliott, Swartz, and Herbane 2010). Even in times of crises, companies' actions are predominantly motivated by long-term prot, which is why they put the strongest emphasis on the protection of their assets and fast recovery of their business processes. In doing so, some factors are directly controllable by the company while others are not (Macdonald and Corsi 2013; Horwitz 2009; Li and Hong 2019; Palin 2017; Rifai 2018).

CSR is a company's involvement in social topics under the expectation that social improvement will lead to long-term prot (Horwitz 2009; van Wassenhove 2006). CSR eorts of private rms are proven means to improve corporate reputation (Donia, Tetrault Sirsly, and Ronen 2017). Reputation implies both the prominence of a company – the label as being known for something - and the image in the sense of holding a generalized favorability towards other companies (Lange, Lee, and Dai 2011). Through CSR related actions like food donations, rm reputation might increase in or after crisis situations (Cozzolino 2012; Dani and Deep 2010; Tomasini and Van Wassenhove 2009). Next to positive reputation, Binder and Witte (2007) name improvement of

government relations, sta motivation and the "desire to do good" as motivation for the private sector to engage. However, Izumi and Shaw (2015) emphasize that companies would also indirectly protect themselves by being involved in crisis response and thereby mitigating crisis eects that would aect the economy, like loss of life or economic downturn. It shows that emergency logistics is included in both, BCM and CSR. The specic concept of reputation is discussed later in the game-theory part in Section A.5.1.3.

In the following, we present two real-life examples, where the private sector faced a crisis. The rst example is the contamination of tap water in the city of Heidelberg, Germany, on February 7th, 2019 (Heidelberg24 2019). The duration of the event was uncertain in the beginning. Hence, people started to hoard bottled water and buy large amounts from retail stores, which in turn had to be relled as soon as possible (Heidelberg24 2019). A sudden increase of demand aects dierent stages in the supply chain, which can cascade along the supply chain (Kildow 2011; Snyder et al. 2016). In Figure A.2, we visualized a commercial bottled water supply chain facing a tap water failure. In personal discussions with companies from food supply chains, we found that in case of sudden demand peaks, rush orders are one measure to quickly rell warehouses and retail stores. However, rush orders would involve higher costs. Another measure would be to skip handling steps in the transport chain in order to oer larger amounts faster to customers. Here, additional coordination eorts would again cause higher costs. The case of Heidelberg shows how commercial retail supply chains can be aected by crisis situations without being directly hit. Moreover, companies' stock values might decline when announcing supply chain disruptions (Dani and Deep 2010).

A second intensively discussed example of private sector donations during a crisis is Walmart's response to Hurricane Katrina in 2005. The retailer donated food, drinks and other goods fast and eciently in the aected area (Horwitz 2009). Not only in this case, supply speed compared to governmental response is seen as a core strength of private actors in crisis response (Nurmala, Vries, and Leeuw 2018). This goes along with ndings from Dani and Deep (2010), who found that supply chain collaboration can help move goods faster and more eciently during crisis.

The above examples highlight the important role of private companies during crises. However, after Hurricane Katrina, Walmart rejected the government's oer to become an "emergency merchandise supplier" (Chen et al. 2013).

Among others, the huge capacities in such a business and large inventories for disaster preparedness did not t with Walmart's corporate strategy. The authors suggest that Walmart's decline was further due to risks perceived with a contractual agreement with a strong partner, which could impede its operational freedom (Chen et al. 2013). This further hints at the importance to take the risks and incentives of the PPEC-partners into account.

The examples show that improvisation and speed are crucial for companies' ecient crisis management. The necessity to immediately react and adapt to new circumstances by possibly re-engineering supply chain processes indicates the exibility of the corresponding processes. Thus, the more exible a company's processes, the more resilient it is towards disruptions (Scholten, Sharkey Scott, and Fynes 2014; Snyder et al. 2016; Tomasini and Van Wassenhove 2009; Tukamuhabwa et al. 2015). Usually, companies would lack preparation for disruptions of low probability and high consequences (Pettit, Fiksel, and Croxton 2010; Izumi and Shaw 2015; van Wassenhove 2006) and focus on rather internal disruptions they can control (Kildow 2011). Consequently, companies might acquire knowledge during a crisis from which they can benet afterwards. Furthermore, collaboration with public actors can provide access to up-to-date information during a crisis with numerous uncertainties (Wiens et al. 2018). Not only access to information, but also the involvement in governmental resource control can be benecial.

# **A.4. Modeling PPECs: logistical challenges**

While supply chain collaboration aims to decrease uncertainty and increase ef ciency, it is also confronted with multiple challenges hampering the achievement of these goals. In the next two sections, challenges associated with modeling and coordinating collaborations, in a commercial and an emergency context respectively, are reviewed and discussed.

### **A.4.1. Collaboration in logistics**

The main goal of all commercial partnerships is to jointly generate value in the exchange relationship that cannot be generated when the rms operate in isolation. However, numerous surveys report that 50 to 70 percent of all these collaborations fail for one reason or another (Schmoltzi and Wallenburg 2011). Because every partner remains independent, the risk of opportunism remains real.

According to Verdonck (2017), challenges related to sustainable partnerships can be divided into six groups - partner selection and reliability, identication and division of joint benets, balance of negotiation power, information and communication technology (ICT), determination of operational scope and competition legislation.

A rst challenge in the establishment of a sustainable horizontal collaboration refers to the selection of suitable partners. The analysis of the strategic and organizational capabilities of a potential partner requires knowledge about its physical and intangible assets, its competencies and skills and its main weaknesses. This type of information is often held private in the respective organization. Moreover, the amount of attainable collaborative savings is inuenced by the degree of t between the collaboration participants. When partners have been selected and the partnership has been established, uncertainty about partner reliability and their commitment to promises also contribute signicantly to the complexity of the collaboration (Verdonck 2017).

Next, it appears that partnering companies nd it dicult to determine and divide the benets of collaborating. It is essential, however, to ensure a fair allocation mechanism in which the contributions of each partner are quantied and accounted for, since this should induce partners to behave according to the collaborative goal and may improve collaboration stability (Wang and Kopfer 2011). Besides selecting a mechanism to share collaborative benets and costs, deciding on the operational and practical organisation of a collaboration might turn out to be a challenging task (Verstrepen et al. 2009). Partnering companies need to agree on the collaboration strategy, the allocation of resources and the applicable key performance indicators (KPIs), among others (Martin et al. 2018).

Another threat to the sustainability of a collaboration is the evolution of the relative bargaining power of the participating companies over the lifetime of the collaboration (Cruijssen, W. Dullaert, and Fleuren 2007).

A fth challenge in the establishment of sustainable collaborations deals with the implementation of the necessary supporting ICT, which could hamper those forms of collaboration that require intensive data exchange (Cruijssen, W. Dullaert, and Fleuren 2007).

Finally, companies engaging in a collaboration project need to consider the applicable legislation on market competition. Legally binding rules prevent companies from working too closely together as this may restrict competition on the market at hand. European competition rules not only prohibit explicit collaborations, such as price-setting agreements, production limits or entry barriers, but also forbid any multi-company arrangements that have similar eects (Verdonck 2017).

### **A.4.2. Collaboration in emergency logistics**

We developed a framework that originates from several (review) papers, which set up frameworks for humanitarian logistics or commercial supply chains facing risks or disruptions. The rst (Kochan and Nowicki 2018; Scholten, Sharkey Scott, and Fynes 2014; Snyder et al. 2016; Swanson and R. J. Smith 2013; Tukamuhabwa et al. 2015) and second category (Scholten, Sharkey Scott, and Fynes 2014; Snyder et al. 2016; Tukamuhabwa et al. 2015) are often discussed topics in literature. These two categories are expanded with the consideration of dierent characteristics of public and private actors in the context of emergency logistics. Assuming PPECs are coordinated and managed indirectly through the use of game-theoretical methods like (relational) contract design (see Section A.5), they are confronted with the following challenges: dierences in strategies and motivations, complex and uncertain interactions between actors, and dierent characteristics of the actors' resources and capabilities (see also Figure A.3).

We will address all these aspects in the following subsections, while a detailed game-theoretical discussion of PPECs follows in Section A.5.

### **A.4.2.1. Strategy and motivation**

Public and private actors engaged in an emergency collaboration are driven by dierent strategies and motivations. These aspects are reected by their dierent general objectives and opposing time horizons of decision making.

### Multi-objective nature of logistic models

The long-term prot and eciency orientation of the private sector is mainly modelled through a cost focus (Holguín-Veras, Jaller, et al. 2012). This is

Figure A.3: Interdependencies in Public-Private Emergency Logistics.