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Ant colony optimization for optimum service times in a Bernoulli schedule vacation interruption queue with balking and reneging
October  2016, 12(4): 1215-1225. doi: 10.3934/jimo.2016.12.1215

## 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, Kuwait 2 Department of Engineering Management and Systems Engineering, Old Dominion University, Norfolk, VA, United States 3 Department of Civil Engineering, Lebanese American University, Byblos, Lebanon

Received  March 2014 Revised  October 2015 Published  January 2016

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.
Citation: Jean-Paul Arnaout, Georges Arnaout, John El Khoury. Simulation and optimization of ant colony optimization algorithm for the stochastic uncapacitated location-allocation problem. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1215-1225. doi: 10.3934/jimo.2016.12.1215
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