Abstract
Product recycling issues have gained increasing attention in many industries in the last decade due to a variety of reasons driven by environmental, governmental and economic factors. Closed-loop supply chain (CLSC) models integrate the forward and reverse flow of products. Since the optimization of these CLSC models is known to be NP-Hard, competition on optimization quality in terms of solution quality and computational time becomes one of the main focuses in the literature in this area. A typical six-level closed-loop supply chain network is examined in this paper, which has great complexity due to the high level of echelons. The proposed solution uses a multi-agent and priority based approach which is embedded within a two-stage Genetic Algorithm (GA), decomposing the problem into (i) product flow, (ii) demand allocation and (iii) pricing bidding process. To test and demonstrate the optimization quality of the proposed algorithm, numerical experiments have been carried out based on the well-known benchmarking network. The results prove the reliability and efficiency of the proposed approach compared to LINGO and the benchmarking algorithm discussed in the literature.
Highlights
The closed-loop supply chain (CLSC) has become more popular in recent years due to several reasons
The Closed-loop supply chain (CLSC) network links the forward logistics and reverse logistics, in which the reverse part is connected by the action of customer recovery
Lee and Lee [26] proposed an optimization approach integrate fuzzy control with genetic algorithm to solve a CLSC model, which efficiency was demonstrated by a real data experiment
Summary
The closed-loop supply chain (CLSC) has become more popular in recent years due to several reasons. The CLSC network links the forward logistics and reverse logistics, in which the reverse part is connected by the action of customer recovery It focuses on the design of transportation routes and decisions on the facility operational state, instead of investigation into the interactions between demands and returns or uncertainties [10,11]. Chan and Chung [16] proposed a two-stage priority based GA and implemented a basic scale experiment to show the feasibility of overcoming the above mentioned problem In this investigation, a priority and multi-agent based two-stage encoding GA approach is developed, which can resolve the focused CLSC network optimized problem. A priority and multi-agent based two-stage encoding GA approach is developed, which can resolve the focused CLSC network optimized problem This algorithm consists of two stages, decomposing the CLSP into two sub-problems. The results show that the proposed approach can be successfully used to solve the integrated closed-loop problem
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