Browsing by Subject "Shelter site location"

Now showing 1 - 3 of 3

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
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
Shelter site location under multi-hazard scenarios
(Elsevier, 2019) Özbay, Ekmel; Çavuş, Özlem; Kara, Bahar Y.
Natural disasters may happen successively in close proximity of each other. This study locates shelter sites and allocates the affected population to the established set of shelters in cases of secondary disaster(s) following the main earthquake, via a three-stage stochastic mixed-integer programming model. In each stage, before the uncertainty in that stage, that is the number of victims seeking a shelter, is resolved, shelters are established, and after the uncertainty is resolved, affected population is allocated to the established set of shelters. The assumption on nearest allocation of victims to the shelter sites implies that the allocation decisions are finalized immediately after the location decisions, hence both location and allocation decisions can be considered simultaneously. And, when victims are allocated to the nearest established shelter sites, the site capacities may be exceeded. To manage the risk inherit to the demand uncertainty and capacities, conditional value-at-risk is utilized in modeling the risk involved in allocating victims to the established shelter sites. Computational results on Istanbul dataset are presented to emphasize the necessity of considering secondary disaster(s), along with a heuristic solution methodology to improve the solution qualities and times.