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

Jean-Paul  Arnaout; Georges  Arnaout; John El  Khoury

doi:10.3934/jimo.2016.12.1215

Article Contents

2016, Volume 12, Issue 4: 1215-1225. Doi: 10.3934/jimo.2016.12.1215

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

1.
Business Administration Department, Gulf University for Science and Technology
2.
Department of Engineering Management and Systems Engineering, Old Dominion University, Norfolk, VA
3.
Department of Civil Engineering, Lebanese American University, Byblos

Received: February 28, 2014

Revised: September 30, 2015

Published: September 2016

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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.

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Mathematics Subject Classification: 68U20, 90C27, 90C59, 90B80, 90B15.

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