A closed-loop logistic model with a spanning-tree based genetic algorithm

Due to the problem of global warming, the green supply chain management, in particular, closed-loop logistics, has drawn the attention of researchers. Although there were logistics models that were examined in the literatures, most of them were case based and not in a closed-loop. Therefore, they lacked generality and could not serve the purposes of recycling, reuse and recovery required in a green supply chain. In this study, the integration of forward and reverse logistics was investigated, and a generalized closed-loop model for the logistics planning was proposed by formulating a cyclic logistics network problem into an integer linear programming model. Moreover, the decisions for selecting the places of manufactories, distribution centers, and dismantlers with the respective operation units were supported with the minimum cost. A revised spanning-tree based genetic algorithm was also developed by using determinant encoding representation for solving this NP model. Numerical experiments were presented, and the results showed that the proposed model and algorithms were able to support the logistic decisions in a closed-loop supply chain efficiently and accurately. Statement of scope and purposes This study concerns with operations of 3R in the green supply chain logistics and the location selection optimization. Based on 'cradle to cradle' principle of a green product, a closed-loop structure of a network was proposed in order to integrate the environmental issues into a traditional logistic system. Due to NP-hard nature of the model, a Genetic Algorithm, which is based on spanning tree structure was developed. Test problems from the small size for accuracy to the large scale for efficiency have been demonstrated with comparison. The promising results have shown the applicability of the proposed model with the solution procedure. (C) 2009 Elsevier Ltd. All rights reserved.