Rahmaniani, R., Ghaderi, A., Saidi Mehrabad, M. (2012). A Simulated Annealing Algorithm within the Variable Neighbourhood Search Framework to Solve the Capacitated Facility Location-Allocation
Problem. Journal of Optimization in Industrial Engineering, 5(10), 45-54.
Ragheb Rahmaniani; abdosalam Ghaderi; Mohammad Saidi Mehrabad. "A Simulated Annealing Algorithm within the Variable Neighbourhood Search Framework to Solve the Capacitated Facility Location-Allocation
Problem". Journal of Optimization in Industrial Engineering, 5, 10, 2012, 45-54.
Rahmaniani, R., Ghaderi, A., Saidi Mehrabad, M. (2012). 'A Simulated Annealing Algorithm within the Variable Neighbourhood Search Framework to Solve the Capacitated Facility Location-Allocation
Problem', Journal of Optimization in Industrial Engineering, 5(10), pp. 45-54.
Rahmaniani, R., Ghaderi, A., Saidi Mehrabad, M. A Simulated Annealing Algorithm within the Variable Neighbourhood Search Framework to Solve the Capacitated Facility Location-Allocation
Problem. Journal of Optimization in Industrial Engineering, 2012; 5(10): 45-54.
A Simulated Annealing Algorithm within the Variable Neighbourhood Search Framework to Solve the Capacitated Facility Location-Allocation
Problem
1MSc, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
2Assistant Professor, Department of Industrial Engineering, University of Kurdistan, P.C. 66177-15177, Sanandaj, Iran
3Professor, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract
In this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which 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 subject to the robustness constraint. To tackle the problem efficiently and effectively, an efficient hybrid solution algorithm based on several meta-heuristics and an exact algorithm is put forward. 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 CPLEX solver.
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