Rahmaniani, R., Saidi-Mehrabad, M., Ghaderi, A. (2018). An Efficient Hybrid Solution Algorithm for the Capacitated Facility Location-Allocation Problem under Uncertainty. Journal of Optimization in Industrial Engineering, (), -. doi: 10.22094/joie.2018.538339

Ragheb Rahmaniani; Mohammad Saidi-Mehrabad; Abdolsalam Ghaderi. "An Efficient Hybrid Solution Algorithm for the Capacitated Facility Location-Allocation Problem under Uncertainty". Journal of Optimization in Industrial Engineering, , , 2018, -. doi: 10.22094/joie.2018.538339

Rahmaniani, R., Saidi-Mehrabad, M., Ghaderi, A. (2018). 'An Efficient Hybrid Solution Algorithm for the Capacitated Facility Location-Allocation Problem under Uncertainty', Journal of Optimization in Industrial Engineering, (), pp. -. doi: 10.22094/joie.2018.538339

Rahmaniani, R., Saidi-Mehrabad, M., Ghaderi, A. An Efficient Hybrid Solution Algorithm for the Capacitated Facility Location-Allocation Problem under Uncertainty. Journal of Optimization in Industrial Engineering, 2018; (): -. doi: 10.22094/joie.2018.538339

An Efficient Hybrid Solution Algorithm for the Capacitated Facility Location-Allocation Problem under Uncertainty

Articles in Press, Accepted Manuscript , Available Online from 14 February 2018

^{1}Department of Industrial Engineering, Iran University of science & technology, Tehran, Iran

^{2}Department of Industrial Engineering, Iran University of science & technology, Tehran, Iran

^{3}Department of Industrial Engineering, Iran University of science & technology, Tehran, Iran

Abstract

In this study, we discuss the capacitated facility location-allocation problem under uncertainty where the uncertainty is characterized by given finite numbers of scenarios. In this model, the objective function minimizes the total expected costs of transportation and opening facilities, while relative regret in each scenario is restricted. To tackle the problem efficiently and effectively, an efficient hybrid solution algorithm based on several meta-heuristics and an exact algorithm is developed. This algorithm generates neighborhoods by combining the main concepts of variable neighborhood search, simulated annealing, and tabu search and finds the local optima by using an algorithm that uses an exact method in its framework. Finally, to test the algorithms’ performance, we apply numerical experiments on both randomly generated and standard test problems. Computational experiments show that our algorithm is more effective and efficient in term of CPU time and solutions quality in comparison with the CPLEX solver.

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