Browsing by Subject "Humanitarian logistics"

Now showing 1 - 13 of 13

Open Access
Covering vehicle routing problem: application for mobile child friendly spaces for refugees
(Springer, 2021-02) Buluc, Elfe; Peker, Meltem; Kara, Bahar Y.; Dora, M.
The world is facing a large-scale refugee crisis because of the ongoing war in Syria, and it is important to improve refugees’ life conditions from a humanitarian point of view. In order to analyse the living conditions of refugees, we conduct fieldwork in a district in Ankara, Turkey, and interview refugees, the local population and humanitarian practitioners from several organizations. Among the many challenges refugees face, we observe that addressing the problems of refugee children is critical. Thus, in this study, we focus on increasing the efficiency of the education services provided to refugee children. We investigate a service provided via mobile trucks that supply informal education and psychological support to children. By analysing the operational dynamics of these trucks, we introduce two problems to the logistics literature, which we refer to as the covering vehicle routing problem and the covering vehicle routing problem with integrated tours. In the first problem, we either visit or cover all nodes, such that every node not in one of the tours is within a predetermined distance of any visited node. In the second problem, we generate smaller tours for covered (or unvisited) nodes originated at the visited ones. We first propose mathematical models for the problems and then introduce heuristic methods to overcome the computational challenge of the second problem. In the computational study, we compare the optimal solutions obtained using the models with a solution of real-life application. We then test the models and heuristics on medium and large real data sets gathered from Turkey and conduct sensitivity analysis on the model parameters.
Open Access
Debris removal during disaster response: a case for Turkey
(Elsevier, 2016) Sahin, H.; Kara, B. Y.; Karasan, O. E.
Debris occurs from the ruin and wreckage of structures during a disaster. Proper removal of debris is of great importance because it blocks roads and prohibits emergency aid teams from accessing disaster-affected regions. Poor disaster management, lack of efficiency and delays in debris removal cause disruptions in providing shelter, nutrition, healthcare and communication services to disaster victims, and more importantly, result in loss of lives. Due to the importance of systematic and efficient debris removal from the perspectives of improving disaster victims quality of life and allowing the transportation of emergency relief materials, the focus of this study is on providing emergency relief supplies to disaster-affected regions as soon as possible by unblocking roads through removing the accumulated debris. We develop a mathematical model for the problem that requires long CPU times for large instances. Since it is crucial to act quickly in an emergency case, we also propose a heuristic methodology that solves instances with an average gap of 1% and optimum ratio of 80.83%.
Embargo
Drones for relief logistics under uncertainty after an earthquake
(Elsevier BV, 2023-03-03) Dükkancı, Okan; Koberstein, Achim; Kara, Bahar Y.
This study presents a post-disaster delivery problem called the relief distribution problem using drones under uncertainty, in which critical relief items are distributed to disaster victims gathered at assembly points after a disaster, particularly an earthquake. Because roads may be obstructed by debris after an earthquake, drones can be used as the primary transportation mode. As the impact of an earthquake cannot be easily predicted, the demand and road network uncertainties are considered. Additionally, the objective is to minimize the total unsatisfied demand subject to a time-bound constraint on the deliveries, as well as the range and capacity limitations of drones. A two-stage stochastic programming and its deterministic equivalent problem formulations are presented. The scenario decomposition algorithm is implemented as an exact solution approach. To apply this study to real-life applications, a case study is conducted based on the western (European) side of Istanbul, Turkey. The computational results are used to evaluate the performance of the scenario decomposition algorithm and analyze the value of stochasticity and the expected value of perfect information under different parametric settings. We additionally conduct sensitivity analyses by varying the key parameters of the problem, such as the time-bound and capacities of the drones.
Open Access
Fair allocation of in-kind donations in post-disaster phase
(2024-05) Varol, Zehranaz
Disaster response aims to address the immediate needs of the affected populations quickly in highly uncertain circumstances. In disaster relief supply chains, the demand comes from disaster victims (typically considered as internally dis-placed populations), while the supply mostly consists of in-kind donations. This dissertation focuses on finding a fair mechanism to distribute a scarce relief item among a set of demand points under supply uncertainty. Primary concerns, restrictive elements, and unknown parameters change throughout the response phase, which substantially affects the structure of the underlying problems. Thus, the first part of this study provides a temporal classification of disaster response (e.g., into subphases) based on evolving features of demand and supply. As the next step, a donation management problem is structured considering the characteristics of a selected subphase. We first focus on the deterministic donation management problem, which is formulated as a multi-criteria multi-period location-inventory problem with service distance constraints. A set of mobile facilities, called points of distribution (PoDs), is used to distribute the collected supply. In particular, two decisions are made for every period of the planning horizon: (i) where to locate a limited number of mobile PoDs and (ii) what quantity to deliver to each demand node from each PoD. We consider three criteria. The first two involve the so-called deprivation cost, which measures a population’s “suffering” due to a shortage. The third objective is related to the total travel time. Two resulting vectorial optimization models are solved using the ε-constraint method, and the corresponding Pareto frontiers are obtained. Computational results are presented that result from applying the proposed methodological developments to an instance of the problem using real data as well as a generated one. Finally, the stochastic counterpart of the problem is addressed with the aim of minimizing a deprivation cost-based objective. The uncertain supply parameters are integrated into the model using a multi-stage stochastic programming (MSSP) approach. The MSSP model is tested on a real data set to assess and evaluate possible policies that can be adopted by decision-makers. Two matheuristic approaches are employed to handle the exponential growth of the scenario trees: a rolling horizon algorithm and a scenario tree reduction algorithm. A set of computational experiments is performed to evaluate the performance of the proposed methodologies. Overall, the results show that the proposed algorithms can better support the decision-making process when fairness is of relevance.
Open Access
Humanitarian facility location under uncertainty: Critical review and future prospects
(Elsevier, 2021-01-08) Dönmez, Zehranaz; Yetiş Kara, Bahar; Karsu, Özlem; Saldanha-da-Gama, F.
This paper provides a comprehensive review of the research done on facility location problems under uncertainty in a humanitarian context. The major goal is to summarize and help structuring this topic, which has increasingly attracted the attention of the scientific community. The literature is reviewed from different perspectives namely, in terms of the type of facilities involved, the decisions to make, the criteria to optimize, the paradigm used for capturing uncertainty, and the solution method adopted. The detailed analysis provided in the manuscript also contributes to identifying the distinguishing features of the problems in the topic. An outcome of the state-of-the-art presented is the identification of the current research trends, expectations and holes in the existing knowledge thus highlighting relevant research directions.
Open Access
Humanitarian logistics under uncertainty: planning for sheltering and evacuation
(Springer Cham, 2023-05-09) Bayram, V.; Y. Kara, Bahar; Saldanha-da-Gama, F.; Yaman, H.; Eiselt, H. A.; Marianov, V.
This chapter focuses on a major area emerging in the context of humanitarian logistics: emergency evacuation planning and management. Two major aspects are covered: shelter site location and evacuation traffic assignment. Both are discussed separately before an integrated problem is considered. Throughout the chapter, uncertainty in the underlying parameters is assumed. The major sources of uncertainty analyzed are the demand for sheltering and capacity of the edges in the underlying network. Congestion issues emerge in this context that are also considered. Different paradigms for capturing uncertainty are considered for illustrative purposes, namely, robust optimization, chance-constrained programming, and stochastic programming.
Open Access
Locating temporary shelter areas after an earthquake: a case for Turkey
(Elsevier, 2015) Kılcı, F.; Kara, B. Y.; Bozkaya, B.
In this study, we propose a mixed integer linear programming based methodology for selecting the location of temporary shelter sites. The mathematical model maximizes the minimum weight of open shelter areas while deciding on the location of shelter areas, the assigned population points to each open shelter area and controls the utilization of open shelter areas. We validate the mathematical model by generating a base case scenario using real data for Kartal, Istanbul, Turkey. Also, we perform a sensitivity analysis on the parameters of the mentioned mathematical model and discuss our findings. Lastly, we perform a case study using the data from the 2011 Van earthquake.
Open Access
The location and location-routing problem for the refugee camp network design
(Elsevier Ltd, 2021-01) Arslan, O.; Culhan Kumcu, G.; Yetiş Kara, Bahar; Laporte, G.
The refugee crisis is one of the major challenges of modern society. The influxes of refugees are usually sudden and the refugees are in sheer need of services such as health care, education and safety. Planning public services under an imminent humanitarian crisis re quires simultaneous strategic and operational decisions. Inspired by a real-world problem that Red Crescent is facing in Southeast Turkey, we study the problem of locating refugee camps and planning transportation of public service providers from their institutions to the located camps. Our modeling approach brings a new facet to the location and routing problem by considering the location of beneficiaries as variables. We develop a branch price-and-cut algorithm for the problem. To solve the pricing problem, we introduce a cycle-eliminating algorithm using nested recursion to generate elementary hop constrained shortest paths. The best version of our algorithm efficiently solves 244-node real-world in stances optimall
Open Access
Logistics planning of cash transfer to Syrian refugees in Turkey
(Elsevier B.V., 2021-05-06) Kian, Ramez; Erdoğan, Güneş; de Leeuw, Sander; Salman F., Sibel; Sabet, Ehsan; Kara, Bahar Y.; Demir, Muhittin H.
This paper addresses a humanitarian logistics problem connected with the Syrian refugee crisis. The ongoing conflict in Syria has caused displacement of millions of people. Cash-based interventions play an important role in aiding people in the post-crisis period to enhance their well-being in the medium and longer term. The paper presents a study on how to design a network of administrative facilities to support the roll-out of cash-based interventions. The resulting multi-level network consists of a central registration facility, local temporary facilities, mobile facilities and vehicles for door-to-door visits. The goal is to reach the maximum number of eligible beneficiaries within a specified time period while minimizing logistics costs, subject to a limit on total security risk exposure. A mixed integer programming model is formulated to optimize the inter-related facility location and routing decisions under multiple objectives. The authors develop a hierarchical multi-objective metaheuristic algorithm to obtain efficient solutions. An application of the model and the solution algorithm to real data from a region in the southeast of Turkey is presented, with associated managerial insights. © 2021 The Author(s)
Open Access
Modeling the shelter site location problem using chance constraints: a case study for Istanbul
(Elsevier B.V., 2018) Kınay, Ö. B.; Kara, Bahar Yetiş; Saldanha-da-Gama, F.; Correia, I.
In this work, we develop and test a new modeling framework for the shelter site location problem under demand uncertainty. In particular, we propose a maxmin probabilistic programming model that includes two types of probabilistic constraints: one concerning the utilization rate of the selected shelters and the other concerning the capacity of those shelters. By invoking the central limit theorem we are able to obtain an optimization model with a single set of non-linear constraints which, nonetheless, can be approximated using a family of piecewise linear functions. The latter, in turn, can be modeled mathematically using integer variables. Eventually, an approximate model is obtained, which is a mixed-integer linear programming model that can be tackled by an off-the-shelf solver. Using the proposed reformulation we are able to solve instances of the problem using data associated with the Kartal district in Istanbul, Turkey. We also consider a large-scale instance of the problem by making use of data for the whole Anatolian side of Istanbul. The results obtained are presented and discussed in the paper. They provide clear evidence that capturing uncertainty in the shelter site location problem by means of probabilistic constraints may lead to solutions that are much different from those obtained when a deterministic counterpart is considered. Furthermore, it is possible to observe that the probabilities embedded in the probabilistic constraints have a clear influence in the results, thus supporting the statement that a probabilistic programming modeling framework, if appropriately tuned by a decision maker, can make a full difference when it comes to find good solutions for the problem.
Open Access
A new selective location routing problem: educational services for refugees
(2022-07) Demir, Şebnem Manolya
Syrian War has forced 5.5 million Syrians to seek for asylum. Turkey hosts 3.7 million Syrian refugees, 47% of whom are children. Even though the schooling rate of Syrian refugee children has steadily increased, currently, there are still more than 400 thousand children distanced from education. Turkey’s initial plans were not accounting for a refugee crisis going on for a decade. In this study, we first identify the availability and accessibility challenges posed by the country’s existing plans of integrating refugees to the national education system. Then, to reinforce schooling access for the refugee children in Turkey, we develop a planning strategy that is aligned with the local regulations. To improve school enrollment rates among Syrian refugee children without burdening the existing infrastructure of the host country, we formulate Capacitated Maximal Covering Problem with Heterogenity Constraints (CMCP-HC) and two extensions: Cooperative CMCP-HC (CCMCP-HC) to improve the current schooling access in Turkey and Modular CCMCP-HC to provide a guide for early planning in the case of a future crisis. As lack of school accessibility has been identified as one of the significant challenges hampering the school attendance rates, we incorporate routing decisions. To ease children’s transportation to schools, we propose a new Selective Location Routing Problem (SLRP) that corresponds to a novel formulation, where the location decisions impact the selective nature of the routing problem. For cases with further scarcity of the resources, we introduce Attendance-based SLRP (A-SLRP) and represent children’s attendance behaviors as a gradual decay function of distance. For the solution of these two complex problems, we offer a 2-Stage Solution Approach that yields optimal solutions for A-SLRP. Results of our computational analysis with the real-life data of the most densely refugee populated Turkish province illustrate that CCMCPHC and Modular CMCP-HC improve schooling enrollment rates and capacity utilizations compared to status quo. Moreover, SLRP and A-SLRP enable approximately twice as many children’s continuation to education, compared to the benchmarking formulation. Overall, this study analyzes Turkey’s experience and lessons learned over a decade to provide a road-map based on operations research methodologies, for potential similar situations in the future.
Open Access
On multi-criteria chance-constrained capacitated single-source discrete facility location problems
(Elsevier, 2019) Kınay, Ö. B.; Saldanha-da-Gama, F.; Kara, Bahar Y.
This work aims at investigating multi-criteria modeling frameworks for discrete stochastic facility location problems with single sourcing. We assume that demand is stochastic and also that a service level is imposed. This situation is modeled using a set of probabilistic constraints. We also consider a minimum throughput at the facilities to justify opening them. We investigate two paradigms in terms of multi-criteria optimization: vectorial optimization and goal programming. Additionally, we discuss the joint use of objective functions that are relevant in the context of some humanitarian logistics problems. We apply the general modeling frameworks proposed to the so-called stochastic shelter site location problem. This is a problem emerging in the context of preventive disaster management. We test the models proposed using two real benchmark data sets. The results show that considering uncertainty and multiple objectives in the type of facility location problems investigated leads to solutions that may better support decision making.
Open Access
Post-disaster assessment routing problem
(Elsevier, 2018) Oruç, Buse Eylül; Kara, Bahar Yetiş
In this study, we propose a post-disaster assessment strategy as part of response operations in which effective and fast relief routing are of utmost importance. In particular, the road segments and the population points to perform assessment activities on are selected based on the value they add to the consecutive response operations. To this end, we develop a bi-objective mathematical model that provides damage information in the affected region by considering both the importance of population centers and road segments on the transportation network through using aerial and ground vehicles (drones and motorcycles). The first objective aims to maximize the total value added by the assessment of the road segments (arcs) whereas the second maximizes the total profit generated by assessing points of interests (nodes). Bi-objectivity of the problem is studied with the ϵ-constraint method. Since obtaining solutions as fast as possible is crucial in the post-disaster condition, heuristic methods are also proposed. To test the mathematical model and the heuristic methods, a data set belonging to Kartal district of Istanbul is used. Computational experiments demonstrate that the use of drones in post-disaster assessment contributes to the assessment of a larger area due to its angular point of view. Also, the proposed heuristic methods not only can find a high-quality approximation of the Pareto front but also mitigates the solution time difficulties of the mathematical model.