An effective two-stage stochastic multi-trip location-transportation model with social concerns in relief supply chains

© 2018 Elsevier B.V. The distribution of emergency aid from warehouses to relief centers to satisfy the needs of the victims in the aftermath of a disaster is a complex problem because it requires a rapid response to human suffering when resources are scarce amidst great uncertainty. In order to provide an effective response and use resources efficiently, this paper presents a novel model to optimize location, transportation, and fleet sizing decisions. In contrast with existing models, vehicles can be reused for multiple trips within micro-periods (blocks of hours) and/or over periods (days). Uncertainty regarding demand, incoming supply, and availability of routes is modeled via a finite set of scenarios, using two-stage stochastic programs. ‘Deprivation costs’ are used to represent social concerns and minimized via two objective functions. Mathematical programming based heuristics are devised to enable good-quality solutions within reasonable computing time. Experimental results based on data from the disastrous 2011 floods and landslides in the Serrana Region of Rio de Janeiro, Brazil, show that the model's novel characteristics help get aid faster to victims and naturally enforce fairness in its distribution to disaster areas in a humanitarian spirit

Integrated facility location and capacity planning under uncertainty

We address a multi-period facility location problem with two customer segments having distinct service requirements. While customers in one segment receive preferred service, customers in the other segment accept delayed deliveries as long as lateness does not exceed a pre-specified threshold. The objective is to define a schedule for facility deployment and capacity scalability that satisfies all customer demands at minimum cost. Facilities can have their capacities adjusted over the planning horizon through incrementally increasing or reducing the number of modular units they hold. These two features, capacity expansion and capacity contraction, can help substantially improve the flexibility in responding to demand changes. Future customer demands are assumed to be unknown. We propose two different frameworks for planning capacity decisions and present a two-stage stochastic model for each one of them. While in the first model decisions related to capacity scalability are modeled as first-stage decisions, in the second model, capacity adjustments are deferred to the second stage. We develop the extensive forms of the associated stochastic programs for the case of demand uncertainty being captured by a finite set of scenarios. Additional inequalities are proposed to enhance the original formulations. An extensive computational study with randomly generated instances shows that the proposed enhancements are very useful. Specifically, 97.5% of the instances can be solved to optimality in much shorter computing times. Important insights are also provided into the impact of the two different frameworks for planning capacity adjustments on the facility network configuration and its total cost.publishersversionpublishe

Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem

This paper presents an extension of the covering location problem as a hybrid covering model that utilizes the set covering and maximal covering location problems. The developed model is a multi-period model that considers strategic and tactical planning decisions. Hybrid covering location problem (HCLP) determines the location of the capacitated facilities by using dynamic set covering location problem as strategic decisions and assigns the constructive units of facilities and allocates the demand points by using dynamic modular capacitated maximal covering location problem as tactical decisions. One of the applications of the proposed model is locating first aid centers in humanitarian logistic services that have been addressed by studying a threat case study in Japan. In addition to validating the developed model, it has been compared to other possible combined problems, and several randomly generated examples have been solved. The results of the case study and model validation tests approve that the main hybrid developed model (HCLP) is capable of providing better coverage percentage compared to conventional covering models and other hybrid variants

Public-private collaborations in emergency logistics: A framework based on logistical and game-theoretical concepts

Collaboration in emergency logistics can be beneficial 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 serve as an important chain in the efficient supply of goods given their resources and existing communication networks. Based on this research gap, we contribute to the research field by quantitatively evaluating public–private collaboration in emergency logistics. A framework for public–private emergency collaborations is developed based on logistical and game-theoretical concepts. In addition, we characterize both public and private actors’ possible roles in emergency logistics based on literature research and real cases. Furthermore, we provide a structured overview on existing PPECs and the challenges they are confronted with. The game-theoretic PPEC model created in this paper provides more detailed information into the motivation and incentives of the partners involved in emergency collaborations. Inspired by game-theoretic accounts of conventional public–private partnerships, this model sheds light on the partners’ participation constraints (which define the scope of collaboration), the effects on the outcome if the partners’ contributions are strategic substitutes, and on reputational effects. Finally, we illustrate how a mechanism design approach can be used by the state to transform the firm’s incentives into lower levels of undersupply or deprivation

An integer L-shaped algorithm for the integrated location and network restoration problem in disaster relief

Being prepared for potential disaster scenarios enables government agencies and humanitarian organizations to respond effectively once the disaster hits. In the literature, the two-stage stochastic programming models are commonly employed to develop preparedness plans before anticipated disasters. These models can be very difficult to solve as the complexity increases by several sources of uncertainty and interdependent decisions. In this study, we propose an integer L-shaped algorithm to solve the integrated location and network restoration model, which is a two-stage stochastic programming model determining the number and locations of the emergency response facilities and restoration resources under uncertainty. Our algorithm accommodates the second-stage binary decision variables which are required to indicate undamaged and restored roads of the network that can be used for relief distribution. Our computational results show that our algorithm outperforms CPLEX for the larger number of disaster scenarios as the solution time of our algorithm increases only linearly as the number of scenarios increases

Public-Private Collaborations in Emergency Logistics: A Framework based on Logistical and Game-Theoretical Concepts

Collaboration in emergency logistics can be beneficial 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 efficient 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\u27 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 different objectives in a collaboration, we add a game-theoretical approach to highlight the incentive structure of both parties in such a collaboration. In this way, we contribute to the research field by quantitatively evaluating public-private collaboration in emergency logistics while considering the problem-specific challenge of the parties\u27 different objectives

Математичне моделювання та візуалізація оптимізаційних двоетапних задач екстреної логістики

В інформаційно-аналітичному розділі було розглянуто існуючі методи та алгоритми, потрібні для розв'язання двоетапних задач оптимального розбиття; проведено аналіз існуючих візуалізацій наукових проєктів. В спеціальному розділі було створено математичне моделювання, розглянуто методи і алгоритми розв’язання задач та продемонстровано роботу цих алгоритмів на розв’язанні модельних задач. В експериментально-аналітичному розділі було створено візуалізацію математичної моделі оптимізаційних двоетапних задач.Мета дослідження: моделювання та візуалізація оптимізаційних двоетапних задач екстреної логістики.Практична цінність отриманих у роботі результатів полягає тому, що запропонована модель і алгоритм дозволяє одночасно знаходити місця розташування первинних пунктів збору і зони їх відповідальності, а також розподіляти зібраний ресурс між центрами першого та другого етапів з урахуванням їх місткості й мінімізації транспортних витра

Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation

Reports of successful implementation of humanitarian optimization models in the field are scarce. Incorporating real conditions and the perspective of decision-makers in the analysis is crucial to enhance the practical value and managerial implications. Although it is known that implementation can be hindered by the lack of practitioner input in the structure of the model, its priorities, and the practicality of solution times, the way these aspects have been introduced in humanitarian optimization models has not been investigated. This study looks at the way research has involved practitioners in different aspects of the design of optimization models to promote implementation. It investigates the aspects affecting the implementation of the models and opportunities to guide future optimization contributions. The article introduces a systematic literature review of 105 articles to answer the research questions. The results are contrasted with a multi-criteria decision analysis using responses from Mexican practitioners. The study found that only 10% of the articles involved practitioners for modelling decisions, which was confirmed by a major gap between the objectives used in the literature and the priorities of Mexican practitioners. In terms of swift decision-making, fewer than 22% of the articles surveyed introduced new solution methods to deliver results in a sensible time. The study also identified very limited inclusion of environmental concerns in the objective functions even though these are a priority in the global agenda. These findings are discussed to propose research directions and suggest best practices for future contributions to promote the implementation of humanitarian logistics models

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner