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Simulation and optimization of ant colony optimization algorithm for the stochastic uncapacitated location-allocation problem

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dc.contributor.author El Khoury, John
dc.contributor.author Arnaout, Jean-Paul
dc.contributor.author Arnaout, Georges
dc.date.accessioned 2017-01-05T11:06:19Z
dc.date.available 2017-01-05T11:06:19Z
dc.date.copyright 2016 en_US
dc.identifier.issn 1553-166X en_US
dc.identifier.uri http://hdl.handle.net/10725/4981
dc.description.abstract This study proposes a novel methodology towards using ant colony optimization (ACO) with stochastic demand. In particular, an optimization-simulation-optimization approach is used to solve the Stochastic uncapacitated location-allocation problem with an unknown number of facilities, and an objective of minimizing the fixed and transportation costs. ACO is modeled using discrete event simulation to capture the randomness of customers' demand, and its objective is to optimize the costs. On the other hand, the simulated ACO's parameters are also optimized to guarantee superior solutions. This approach's performance is evaluated by comparing its solutions to the ones obtained using deterministic data. The results show that simulation was able to identify better facility allocations where the deterministic solutions would have been inadequate due to the real randomness of customers' demands. en_US
dc.language.iso en en_US
dc.title Simulation and optimization of ant colony optimization algorithm for the stochastic uncapacitated location-allocation problem en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SOE en_US
dc.author.idnumber 200103005 en_US
dc.author.department Civil Engineering en_US
dc.description.embargo N/A en_US
dc.relation.journal Journal of Industrial and Management Optimization (JIMO) en_US
dc.journal.volume 12 en_US
dc.journal.issue 4 en_US
dc.article.pages 1215-1225 en_US
dc.keywords Location-allocation problem en_US
dc.keywords Metaheuristics en_US
dc.keywords Stochastic simulation en_US
dc.keywords Discrete event simulation en_US
dc.keywords Colony optimization en_US
dc.identifier.doi http://dx.doi.org/10.3934/jimo.2016.12.1215 en_US
dc.identifier.ctation Arnaout, J- P., Arnaout, G., & El Khoury, J. (2016). Simulation and optimization of ant colony optimization algorithm for the stochastic uncapacitated location-allocation problem. Journal of Industrial and Management Optimization, 12(4), 1215 - 1225 en_US
dc.author.email john.khoury@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://www.aimsciences.org/journals/displayArticlesnew.jsp?paperID=12136 en_US
dc.author.affiliation Lebanese American University en_US


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