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An Optimization Model for Reverse Logistics Network under Stochastic Environment Using Genetic Algorithm
Abstract:

Recovery of used products has become increasingly important recently due to economic reasons and growing environmental or legislative concern. Product recovery, which comprises reuse, remanufacturing and materials recycling, requires an efficient reverse logistic network. One of the main characteristics of 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 reverse logistics parameters. With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network. This probabilistic model 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 determines not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfies demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost. Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition. Finally, we apply the proposed model to a numerical example

Keywords: Reverse logistics network, Genetic algorithm (GA), Priority-based encoding, Stochastic programming
Author(s): Mostafa Hosseinzadeh , Emad Roghanian
Source: International Journal of Business and Social Science, Vol. 3 No. 12 [Special Issue – June 2012]
Subject: لجستیک
Category: مقاله مجله
Release Date: 2012
No of Pages: 16
Price(Tomans): 3800
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