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The problem of dynamic relocation and phase-out of combined manufacturing plant and warehousing facilities in the supply chain are concerned. A multiple time/multiple objective model is proposed to maximize total profit during the time horizon, minimize total access time from the planfwarehouse facilities to its suppliers and customers and maximize aggregated local incentives during the time horizon. The relocation problem keeps the feature of NP-hard and with the traditional method the optimal result cannot be got easily. So a compact genetic algorithm (CGA) is introduced to solve the problem. In order to accelerate the convergence speed of the CGA, the least square approach is introduced and a fast compact genetic algorithm (fCGA) is proposed. Finally, simulation results with the fCGA are compared with the CGA and classical integer programming (IP). The results show that the fCGA proposed is of high efficiency for Pareto optimality problem.
The problem of dynamic relocation and phase-out of combined manufacturing plant and warehousing facilities in the supply chain are concerned. A multiple time / multiple objective model is proposed to maximize total profit during the time horizon, minimize total access time from the planf warehouse facilities to its suppliers and customers and maxim aggregated local incentives during the time horizon. The relocation problem keeps the feature of NP-hard and with the traditional method the optimal result can not be got well. So a compact genetic algorithm (CGA) is introduced to solve the problem the result of the convergence of the CGA, the least square approach is introduced and a fast compact genetic algorithm (fCGA) is proposed. Finally, simulation results with the fCGA are compared with the CGA and classical integer programming (IP) The results show that the fCGA proposed is high efficiency for Pareto optimality problem.