Humanitarian facility location under uncertainty: Critical review and future prospects

Dönmez, Zehranaz; Kara, Bahar Y.; Karsu, Özlem; Saldanha-da-Gama, Francisco

doi:10.1016/j.omega.2021.102393

Cited by 62 publications

(26 citation statements)

References 144 publications

(445 reference statements)

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“…Key operational issues have been studied by researchers and linked to the broader operations research literature by referring to well-known location decision problems in collaboration with the Turkish Red Crescent [14]. A recent study thoroughly summarized various types of facility location challenges according to aspects such as the location space (continuous, network-based, discrete), the type of facilities (suppliers, distribution centers, shelters), the critical decision-makings criteria (equity, cost, reliability), and source of uncertainty (supply, demand, network) [15]. Although facility location problems have theoretically been addressed in isolation, in practice, location decisions and positioning challenges are coupled with allocation, inventory, distribution, and routing decisions in disaster relief operation settings [15].…”

Section: Location and Allocation Challenges In Border Operationsmentioning

confidence: 99%

“…A recent study thoroughly summarized various types of facility location challenges according to aspects such as the location space (continuous, network-based, discrete), the type of facilities (suppliers, distribution centers, shelters), the critical decision-makings criteria (equity, cost, reliability), and source of uncertainty (supply, demand, network) [15]. Although facility location problems have theoretically been addressed in isolation, in practice, location decisions and positioning challenges are coupled with allocation, inventory, distribution, and routing decisions in disaster relief operation settings [15]. As it relates to accommodating and processing displaced populations at international borders, scholars must address the same positioning challenges while accounting for the specific aspects of the associated intricacies and corresponding planning complexities.…”

Section: Location and Allocation Challenges In Border Operationsmentioning

confidence: 99%

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Optimization for Secure and Humane Border Operations

Farajzadeh¹,

Trapp²

2022

Preprint

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In 2020 tens of millions of migrants worldwide sought safety due to various push-pull factors such as economic, geographic, demographic, and societal challenges [1,2].Large movements of human flows toward national borders constitute a complex humanitarian crisis that requires appropriate preparations of border resources. Motivated by prevailing national agenda issues for improved border support, we identify common operational challenges along the border and discuss how they can be addressed using advanced analytics. Multiple stakeholders, corresponding goals and complex decisions call for advanced optimization techniques to holistically treat such challenges. This chapter highlights how optimization can be employed to improve both the security and humanitarian aspects of border operations in the context of migration flows.

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Section: Location and Allocation Challenges In Border Operationsmentioning

confidence: 99%

Section: Location and Allocation Challenges In Border Operationsmentioning

confidence: 99%

Optimization for Secure and Humane Border Operations

Farajzadeh¹,

Trapp²

2022

Preprint

View full text Add to dashboard Cite

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“…In such a case, there exists little market information that can be used for accurate demand forecast, and companies may choose to take multiple steps to locate all car parking spaces and/or charging stations for electric vehicles, rather than commit all the resources up front using estimated demand. Lastly, in humanitarian logistics, due to unpredictable and occurring natural disasters, it is often crucial to build temporary health centers, distribution centers, or shelters in a multi-period planning horizon under uncertain demand, supply and network connectivity (Dönmez et al 2021). In this problem context, a two-stage model will decide the facility locations and inventory pre-positioning for all the time periods (in days or weeks) a priori and then adapt the routing and allocation decisions to different scenarios realized in the second stage.…”

Section: Introductionmentioning

confidence: 99%

On the Value of Multistage Risk-Averse Stochastic Facility Location With or Without Prioritization

Lei¹,

Shen²

2021

Preprint

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We consider a multi-period capacitated facility location problem over a finite time horizon under uncertain demand in each period. We formulate a multistage stochastic integer program using a scenario tree representation of the uncertainty, and compare it with a two-stage approach that decides which facilities to open for all periods up front. In the multistage model, in each stage, after realizing the demand, a decision maker optimizes facility locations and product flows from open facilities to demand sites, with the goal of minimizing certain measures of the future random cost associated with locating facilities and satisfying demand.Using expected conditional risk measures (ECRMs), we provide tight lower bounds for the gaps between optimal objective values of risk-averse multistage models and their two-stage counterparts. Moreover, two approximation algorithms are proposed to efficiently solve risk-averse two-stage and multistage stochastic programs, whose approximation ratios are independent of the underlying stochastic process, structure of the scenario tree, number of stages, and risk-preference-related parameters. We conduct numerical studies using both randomly generated and real-world network instances to demonstrate the tightness of the analytical bounds and computational efficacy of the proposed approximation algorithms. We also provide managerial insights for guiding locating facilities with risk aversion in practice, based on various types of uncertainty cases and parameter settings.

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“…Even though equity in allocation of scarce public resources has been a concern of academics and practitioners for decades, there is no consensus on how to mathematically model this concept within humanitarian logistics optimization. Ultimately, the concept of equity per se is subject to different interpretations and is very much context-dependent (Dönmez et al, 2021). However, scholars have identified three key aspects that can guide decision makers and reduce their subjective biases in choosing an appropriate equity modelling approach: identifying an equity metric, constructing an equity function, and formulating an equity objective.…”

Section: Introductionmentioning

confidence: 99%

Revisiting Gini for equitable humanitarian logistics

Alem¹,

Caunhye²,

Moreno³

2021

Preprint

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Modeling equity in the allocation of scarce resources is a fast-growing concern in the humanitarian logistics field. The Gini coefficient is one of the most widely recognized measures of inequity and it was originally characterized by means of the Lorenz curve, which is a mathematical function that links the cumulative share of income to rank-ordered groups in a population. So far, humanitarian logistics models that have approached equity using the Gini coefficient do not actually optimize its original formulation, but use alternative definitions that do not necessarily replicate that original Gini measure. In this paper, we derive the original Gini coefficient via the Lorenz curve to optimize the effectiveness-equity tradeoff in a humanitarian location-allocation problem. We also propose new valid inequalities based on an upper-bounding Lorenz curve to tighten the linear relaxation of our model and develop a clustering-based construction of the Lorenz curve that requires fewer additional constraints and variables than the original one. The computational study, based on the floods and landslides in Rio de Janeiro state, Brazil, reveals that while alternative Gini definitions have interesting properties, they can generate vastly different decisions compared to the original Gini coefficient. In addition, viewed from the perspective of the original Gini coefficient, these decisions can be significantly less equitable.

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Humanitarian facility location under uncertainty: Critical review and future prospects

Cited by 62 publications

References 144 publications

Optimization for Secure and Humane Border Operations

Optimization for Secure and Humane Border Operations

On the Value of Multistage Risk-Averse Stochastic Facility Location With or Without Prioritization

Revisiting Gini for equitable humanitarian logistics

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