Recovery of used products has become increasingly important recently due to economic reasons andgrowing environmental or legislative concern. Product recovery, which comprises reuse, remanufactur-ing and materials recycling, requires an efficient reverse logistic network. One of the main characteristicsof reverse logistics network problem is uncertainty that further amplifies the complexity of the problem.The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverselogistics parameters. With consideration of the factors noted above, this paper proposes a probabilisticmixed integer linear programming model for the design of a reverse logistics network. This probabilisticmodel is first converted into an equivalent deterministic model. In this paper we proposed multi-product,multi-stage reverse logistics network problem for the return products to determine not only the subsetsof disassembly centers and processing centers to be opened, but also the transportation strategy that willsatisfy demand imposed by manufacturing centers and recycling centers with minimum fixed openingcost and total shipping cost. Then, we propose priority based genetic algorithm to find reverse logisticsnetwork to satisfy the demand imposed by manufacturing centers and recycling centers with minimumtotal cost under uncertainty condition. Finally, we apply the proposed model to a numerical example.
کلید واژگان :Reverse logistics network;Genetic algorithm (GA);Priority-based encoding;Stochastic programming
ارزش ریالی : 600000 ریال
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