Journal of Industrial and Management Optimization
This study proposes a novel methodology towards using ant colony optimization (ACO) with stochastic demand. In particular, an optimizationsimulation-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.
Original Publication Citation
Arnaout, J. P., Arnaout, G., & El Khoury, J. (2016). Simulation and optimization of ant colony optimization algorithm for the stochiastic uncapacitated location-allocation problem. Journal of Industrial and Management Optimization, 12(4), 1215-1225. doi:10.3934/jimo.2016.12.1215
Arnaout, Jean-Paul; Arnaout, Georges; and El Khoury, John, "Simulation and Optimization Of Ant Colony Optimization Algorithm For The Stochiastic Uncapacitated Location-Allocation Problem" (2016). Engineering Management & Systems Engineering Faculty Publications. 39.