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.

中文翻译：

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基于多智能体和基于优先权的遗传算法的产品回收闭环供应链研究

在过去的十年中，由于环境，政府和经济因素的推动，产品回收问题在许多行业中受到越来越多的关注。闭环供应链（CLSC）模型集成了产品的正向和反向流动。由于已知这些CLSC模型的优化是NP-Hard，因此就解决方案质量和计算时间而言，优化质量方面的竞争已成为该领域文献中的重点之一。本文研究了典型的六级闭环供应链网络，由于梯队水平高，该网络具有很高的复杂性。提出的解决方案使用了基于多主体和优先级的方法，该方法嵌入了两阶段遗传算法（GA）中，将问题分解为（i）产品流，（ii）需求分配和（iii）定价招标过程。为了测试和证明所提出算法的优化质量，在众所周知的基准网络的基础上进行了数值实验。与LINGO和文献中讨论的基准算法相比，结果证明了该方法的可靠性和效率。