Modeling the shelter site location problem using chance constraints: a case study for Istanbul

Simple item page
buir.contributor.authorKara, Bahar Yetiş
dc.citation.epage145en_US
dc.citation.issueNumber1en_US
dc.citation.spage132en_US
dc.citation.volumeNumber270en_US
dc.contributor.authorKınay, Ö. B.en_US
dc.contributor.authorKara, Bahar Yetişen_US
dc.contributor.authorSaldanha-da-Gama, F.en_US
dc.contributor.authorCorreia, I.en_US
dc.date.accessioned2019-02-21T16:01:33Z
dc.date.available2019-02-21T16:01:33Z
dc.date.issued2018en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractIn 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.
dc.description.sponsorshipThis research has been partially supported by the Turkish Academy of Sciences and by the Portuguese Science Foundation , projects UID/MAT/04561/2013 (CMAF-CIO/FCUL) and UID/MAT/00297/2013 (CMA/FCT/UNL) . The authors wish to thank the anonymous referees for the valuable comments and suggestions provided.
dc.embargo.release2020-10-01en_US
dc.identifier.doi10.1016/j.ejor.2018.03.006
dc.identifier.issn0377-2217
dc.identifier.urihttp://hdl.handle.net/11693/49870
dc.language.isoEnglish
dc.publisherElsevier B.V.
dc.relation.isversionofhttps://doi.org/10.1016/j.ejor.2018.03.006
dc.relation.projectTürkiye Bilimler Akademisi, TÜBA - UID/MAT/04561/2013 (CMAF-CIO/FCUL - UID/MAT/00297/2013
dc.source.titleEuropean Journal of Operational Researchen_US
dc.subjectApproximationsen_US
dc.subjectHumanitarian logisticsen_US
dc.subjectLocationen_US
dc.subjectProbabilistic programmingen_US
dc.subjectShelter site locationen_US
dc.titleModeling the shelter site location problem using chance constraints: a case study for Istanbulen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Modeling_the_shelter_site_location_problem_using_chance_constraints_A_case_study_for_Istanbul (1).pdf
Size:
686.19 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Department of Industrial Engineering