Abstract
In recent years, Reverse Logistics (RL) has become a field of importance for all organizations due to growing environmental concerns, legislation, corporate social responsibility and sustainable competitiveness. In Reverse logistics, the used or returned products are collected after their acquisition and inspected for sorting into the different categories. The next step is to disposition them for repair, remanufacturing, recycling, reuse or final disposal. Manufacturers may adopt reverse logistics by choice or by force, but they have to decide whether performing the activities themselves or outsourcing to a third party (Martin et al., 2010). Lourenço et al., (2003) described three main areas of improvement within the RL process. Firstly, companies can reduce the level of returns through the analysis of their causes. Secondly, they can work on the improvement of the return’s process and, thirdly, they can create value from the returns. This paper considers the multistage reverse Logistics Network Problem (mrLNP) proposed by Lee et al., (2008). With minimizing the total of costs to reverse logistics shipping cost. We will demonstrate the mrLNP model will be formulated as a three-stage logistics network model. Since such network design problems belong to the class of NP-hard problems we propose a Simulated Annealing (SA) and simulated annealing with priority (priSA) with special neighborhood search mechanisms to find the near optimal solution consisting of two stages. Computer simulations show the several numerical examples by using, SA, priSA and priGA(Genetic algorithm with priority-based encoding method) and effectiveness of the proposed method.
Highlights
Recovery of used products and product recovery are becoming increasingly important for economic, environmental or legislative
The model determines the quantity of products/parts processed in the remanufacturing facilities/subcontractors and the amount of parts purchased from the external suppliers while maximizing the total remanufacturing cost saving. (Min et al.,2006) proposed a nonlinear mixed integer programming model and a genetic algorithm that can solve the reverse logistics problem involving product returns
This paper is organized as follows: in Section 2, the mathematical model proposed by Lee et al, (2008)..is introduced, in Section 3,an efficient simulated annealing (SA) algorithm is applied to solve the model with and without adapting the encoding method based on the priority and a dynamic neighborhood search strategy that enhances the performance of the applied algorithm are the main contributions of this paper; Section 4, numerical experiments are presented to demonstrate the effectiveness of the proposed approach; in Section 5, we close our work a little insight on the procedures followed during the entire research and open new perspectives
Summary
Recovery of used products and product recovery are becoming increasingly important for economic, environmental or legislative. Kirkke et al,(1999) presented an MILP model based on a multi-level incapacitated warehouse location model They described a case study, dealing with a reverse logistics network for the returns, processing, and recovery of discarded copiers. (Min et al.,2006) proposed a nonlinear mixed integer programming model and a genetic algorithm that can solve the reverse logistics problem involving product returns. Ko et al, (2007) presented a mixed integer nonlinear programming model for the design of a dynamic integrated distribution network to account for the integrated aspect of optimizing the forward and return network simultaneously They proposed a genetic algorithmbased heuristic for solving this problem. Lee et al, (2008)., Proposed a multi-stage, multi-product, MILP model for minimizing the total of costs to reverse logistics shipping cost and fixed opening cost of facilities They proposed a hybrid genetic algorithm for solving this problem. This paper is organized as follows: in Section 2, the mathematical model (mrLNP) proposed by Lee et al, (2008)..is introduced, in Section 3,an efficient simulated annealing (SA) algorithm is applied to solve the model with and without adapting the encoding method based on the priority and a dynamic neighborhood search strategy that enhances the performance of the applied algorithm are the main contributions of this paper; Section 4, numerical experiments are presented to demonstrate the effectiveness of the proposed approach; in Section 5, we close our work a little insight on the procedures followed during the entire research and open new perspectives
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
