School of Economics and Management, Changzhou Vocational Institute of Textile and Garment, Changzhou, China.
Navigation College, Jimei University, Xiamen, China.
PLoS One. 2024 Jun 27;19(6):e0306294. doi: 10.1371/journal.pone.0306294. eCollection 2024.
Recycling of used products can provide substantial economic and environmental benefits for supply chain players. However, many factors associated with the design of closed-loop supply chain networks are uncertain in their nature, including demand, opening cost of facilities, capacity of opened facilities, transportation cost, and procurement cost. Therefore, this study proposes a novel fuzzy programming model for closed-loop supply chain network design, which directly relies on the fuzzy ranking method based on a credibility measure. The objective of the presented optimization model aims at minimizing the total cost of the network when selecting the facility locations and transportation routes between the nodes of the network. Based on the problem characteristics, a Migratory Birds Optimization Algorithm with a new product source encoding scheme is developed as a solution approach. The inspiration for the product source coding method originates from the label information of raw material supplier and manufacturing factories on product packaging, as well as the information of each logistics node on the delivery order. This novel encoding method aims to address the limitations of four traditional encoding methods: Prüfer number based encoding, spanning tree based encoding, forest data structure based encoding, and priority based encoding, thereby increasing the likelihood of heuristic algorithms finding the optimal solution. Thirty-five illustrative examples are developed to evaluate the proposed algorithm against the exact optimization method (LINGO) and a Genetic Algorithm, Ant Colony Optimization, Simulated Annealing, which are recognized as well-known metaheuristic algorithms. The results from extensive experiments show that the proposed algorithm is able to provide optimal and good-quality solutions within acceptable computational time even for large-scale numerical examples. The suitability of the model is confirmed through a meticulous sensitivity analysis. This analysis involves adjusting the confidence level incrementally from 50% to 100%, in 5% intervals, with respect to the model's uncertain parameters. Consequently, it yields valuable managerial insights. The outcomes of this research are expected to provide scientific support for related supply chain enterprises and stakeholders.
产品的再利用可以为供应链参与者带来显著的经济和环境效益。然而，闭环供应链网络设计的许多因素在本质上是不确定的，包括需求、设施的开启成本、已开启设施的容量、运输成本和采购成本。因此，本研究提出了一种新的模糊规划模型，用于闭环供应链网络设计，该模型直接依赖于基于可信度度量的模糊排序方法。所提出的优化模型的目标是在选择设施位置和网络节点之间的运输路线时，最小化网络的总成本。基于问题的特点，开发了一种具有新的产品源编码方案的候鸟优化算法作为解决方案。产品源编码方法的灵感来自于产品包装上原材料供应商和制造工厂的标签信息，以及交货单上每个物流节点的信息。这种新颖的编码方法旨在解决基于普吕弗数的编码、基于生成树的编码、基于森林数据结构的编码和基于优先级的编码这四种传统编码方法的局限性，从而提高启发式算法找到最优解的可能性。开发了 35 个示例来说明算法，并将其与精确优化方法（LINGO）和遗传算法、蚁群优化、模拟退火进行比较，这些算法被认为是著名的元启发式算法。广泛的实验结果表明，该算法能够在可接受的计算时间内提供最优和高质量的解决方案，即使对于大规模数值示例也是如此。通过细致的敏感性分析确认了模型的适用性。该分析涉及根据模型的不确定参数，逐步将置信水平从 50%增加到 100%，间隔为 5%。因此，它产生了有价值的管理见解。这项研究的结果有望为相关供应链企业和利益相关者提供科学支持。
