Location model for a remanufacturing reverse logistics network based on adaptive genetic algorithm
Energy saving and environmental protection are important issues of today. Concerning the environmental and social need to increase the utilization of used products, this paper introduces two remanufacturing reverse logistics (RL) network models, namely, the open loop model and the closed loop model. In an open loop RL system, used products are recovered by outside firms, while in a closed loop RL system, they are returned to their original producers. The open loop model features a location selection with two layers. For this model, a mixed integer linear program (MILP) is built to minimize the total costs of the open loop RL system, including the fixed cost, the freight between nodes, the operation cost of storage and remanufacturing centers, the penalty cost of unmet or remaining demand quantity, and the government provided subsidy given to the enterprises that protect the environment. This MILP is solved using an adaptive genetic algorithm with MATLAB simulation. For a closed loop RL network model, a special demand function considering the relationship between new and remanufactured products is developed. Remanufacturing rate, environmental awareness, service demand elasticity, value added services, and their impacts on total profit of the closed loop supply chain are analyzed. The closed loop RL network model is proved effective through the analysis of a numerical example.
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