In this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which the uncertainty ischaracterized by given finite numbers of scenarios. In this model, the objective function minimizes the total expected costs oftransportat
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In this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which the uncertainty ischaracterized by given finite numbers of scenarios. In this model, the objective function minimizes the total expected costs oftransportation and opening facilities subject to the robustness constraint. To tackle the problem efficiently and effectively, an efficienthybrid solution algorithm based on several meta-heuristics and an exact algorithm is put forward. This algorithm generates neighborhoodsby combining the main concepts of variable neighborhood search, simulated annealing, and tabu search and finds the local optima by usingan algorithm that uses an exact method in its framework. Finally, to test the algorithms’ performance, we apply numerical experiments onboth randomly generated and standard test problems. Computational experiments show that our algorithm is more effective and efficient interm of CPU time and solutions quality in comparison with CPLEX solver.
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