Browsing by Subject "Disaster management"

Now showing 1 - 9 of 9

Open Access
Debris removal during disaster response phase : a case for Turkey
(2013) Şahin, Halenur
In this study, a methodology to provide emergency relief supplies to the disaster affected regions is developed. As a result of destructive effects of disasters, debris, which is the ruin and wreckage of the structures, occurs. Proper removal of debris has significant importance since it blocks the roads and prohibits emergency aid teams to access the disaster affected regions. Wrong disaster management, lack of efficiency and delays in debris removal cause disruptions in providing sheltering, nutrition, healthcare and communication services to the disaster victims, and more importantly they result in loss of lives. Due to the importance of a systematic and efficient way of debris removal from the point of improving disaster victims’ life quality and its contributions to transportation of emergency relief materials to the disaster affected regions, the focus of this study is providing emergency relief supplies to the disaster affected regions as soon as possible, by considering unblocking operations of roads through removing the accumulated debris. To come up with a scientific solution methodology to the problem, mathematical models that select the paths in order to transport emergency aid materials in the presence of debris to the pre-determined disaster affected regions are developed. The performances of the models are tested on two distinct data sets from İstanbul. Since it is crucial to act quickly in an emergency case, a constructive and an improvement heuristic are also proposed.
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%.
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
Nonlinear mixed integer programming models and algorithms for fair and efficient large scale evacuation planning
(2015-07) Bayram, Vedat
Shelters are safe facilities that protect a population from possible damaging effects of a disaster. Traffic management during an evacuation and the decision of where to locate the shelters are of critical importance to the performance of an evacuation plan. From the evacuation management authority's point of view, the desirable goal is to minimize the total evacuation time by computing a system optimum (SO). However, evacuees may not be willing to take long routes enforced on them by a SO solution; but they may consent to taking routes with lengths not longer than the shortest path to the nearest shelter site by more than a tolerable factor. We develop a model that optimally locates shelters and assigns evacuees to the nearest shelter sites by assigning them to shortest paths, shortest and nearest with a given degree of tolerance, so that the total evacuation time is minimized. As the travel time on a road segment is often modeled as a nonlinear function of the ow on the segment, the resulting model is a nonlinear mixed integer programming model. We develop a solution method that can handle practical size problems using second order cone programming techniques. Using our model, we investigate the trade-of between efficiency and fairness. Disasters are uncertain events. Related studies and real-life practices show that a significant uncertainty regarding the evacuation demand and the impact of the disaster on the infrastructure exists. The second model we propose is a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to shelters and paths to minimize the expected total evacuation time, under uncertainty. The model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for an impending earthquake in Istanbul, Turkey. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values. We also propose an exact algorithm based on Benders decomposition to solve the stochastic problem. To the best of our knowledge, ours is the first algorithm that uses duality results for second order cone programming in a Benders decomposition setting. We solve practical size problems with up to 1000 scenarios in moderate CPU times. We investigate methods such as employing a multi-cut strategy, deriving pareto-optimal cuts, using a reduced primal subproblem and preemptive priority multiobjective program to enhance the proposed algorithm. Computational results confirm the efficiency of our algorithm. This research is supported by TUBITAK, The Scientific and Technological Research Council of Turkey with project number 213M434.
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.
Open Access
Risk based facility location by using fault tree analysis in disaster management
(Elsevier Ltd, 2015) Akgün, T.; Gümüşbuğa F.; Tansel, B.
Determining the locations of facilities for prepositioning supplies to be used during a disaster is a strategic decision that directly affects the success of disaster response operations. Locating such facilities close to the disaster-prone areas is of utmost importance to minimize response time. However, this is also risky because the facility may be disrupted and hence may not support the demand point(s). In this study, we develop an optimization model that minimizes the risk that a demand point may be exposed to because it is not supported by the located facilities. The purpose is to choose the locations such that a reliable facility network to support the demand points is constructed. The risk for a demand point is calculated as the multiplication of the (probability of the) threat (e.g., earthquake), the vulnerability of the demand point (the probability that it is not supported by the facilities), and consequence (value or possible loss at the demand point due to threat). The vulnerability of a demand point is computed by using fault tree analysis and incorporated into the optimization model innovatively. To our knowledge, this paper is the first to use such an approach. The resulting non-linear integer program is linearized and solved as a linear integer program. The locations produced by the proposed model are compared to those produced by the p-center model with respect to risk value, coverage distance, and covered population by using several test problems. The model is also applied in a real problem. The results indicate that taking the risk into account explicitly may create significant differences in the risk levels. © 2014 Elsevier Ltd.
Open Access
Shelter location and evacuation route assignment under uncertainty: a benders decomposition approach
(INFORMS Inst.for Operations Res.and the Management Sciences, 2018) Bayram, V.; Yaman, Hande
Shelters are safe facilities that protect a population from possible damaging effects of a disaster. For that reason, shelter location and traffic assignment decisions should be considered simultaneously for an efficient evacuation plan. In addition, as it is very difficult to anticipate the exact place, time, and scale of a disaster, one needs to take into account the uncertainty in evacuation demand, the disruption/degradation of evacuation road network structure, and the disruption in shelters. In this study, we propose an exact algorithm based on Benders decomposition to solve a scenario-based two-stage stochastic evacuation planning model that optimally locates shelters and that assigns evacuees to shelters and routes in an efficient and fair way to minimize the expected total evacuation time. The second stage of the model is a second-order cone programming problem, and we use duality results for second-order cone programming in a Benders decomposition setting. We solve practical-size problems with up to 1,000 scenarios in moderate CPU times. We investigate methods such as employing a multicut strategy, deriving Pareto-optimal cuts, and using a preemptive priority multiobjective program to enhance the proposed algorithm. We also use a cutting plane algorithm to solve the dual subproblem since it contains a constraint for each possible path. Computational results confirm the efficiency of our algorithms.
Open Access
Solution methodologies for debris removal in disaster response
(Springer, 2016) Berktaş, N.; Kara, B. Y.; Karaşan, O. E.
During the disaster response phase of the emergency relief, the aim is to reduce loss of human life by reaching disaster affected areas with relief items as soon as possible. Debris caused by the disaster blocks the roads and prevents emergency aid teams to access the disaster affected regions. Deciding which roads to clean to transport relief items is crucial to diminish the negative impact of a disaster on human health. Despite the significance of the problem during response phase, in the literature debris removal is mostly studied in the recovery or the reconstruction phases of a disaster. The aim of this study is providing solution methodologies for debris removal problem in the response phase in which effective and fast relief routing is of utmost importance. In particular, debris removal activities on certain blocked arcs have to be scheduled to reach a set of critical nodes such as schools and hospitals. To this end, two mathematical models are developed with different objectives. The first model aims to minimize the total time spent to reach all the critical nodes whereas the second minimizes the weighted sum of visiting times where weights indicate the priorities of critical nodes. Since obtaining solutions quickly is important in the early post-disaster, heuristic algorithms are also proposed. Two data sets belonging to Kartal and Bakırköy districts of İstanbul are used to test the mathematical models and heuristics.
Open Access
A stochastic programming approach for Shelter location and evacuation planning
(EDP Sciences, 2018) Bayram, V.; Yaman, Hande
Shelter location and traffic allocation decisions are critical for an efficient evacuation plan. In this study, we propose a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to nearest shelters and to shortest paths within a tolerance degree to minimize the expected total evacuation time. Our model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for a potential earthquake in Istanbul. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values