Applying particle swarm optimization algorithm to a multi-retailer supply chain inventory problem

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题名:	Applying particle swarm optimization algorithm to a multi-retailer supply chain inventory problem
作者:	Lu, Chi-jie;Jiang, Dong-ying;Yang, Chih-te
关键词:	Multinational supply chain;inventory;carbon emissions;multiple retailers;particle swarm optimization
日期:	2024-01-24
上传时间:	2024-01-30 12:05:29 (UTC+8)
摘要:	With the growing emphasis on environmental, social, and governance (ESG), the concept of green supply chain inventory management has become crucial for sustainable business operations. Taking into account environmental impact and resource efficiency for the purpose of reducing carbon emissions, the integration of green policies into inventory and production decisions has evolved into a significant issue within current supply chain management. Especially different supply chain members are subjected to varying carbon reduction policies for multinational supply chains. Therefore, this study aims to examine the multi-stage production inventory problem within a multinational supply chain involving a single manufacturer and multiple retailers from different countries under a combination of carbon reduction policies where the manufacturer faces a carbon cap-and-trade policy while all the retailers are subjected to a carbon tax policy. First, the total profits and carbon emissions functions for both the manufacturer and retailers are separately established in three stages: material supply, finished product production and delivery, and order and sales. Subsequently, the optimal material supply, finished product production, delivery, and replenishment strategies for each supply chain member are determined to maximize the integrated total profit of the supply chain system. Next, the corresponding problem has been formulated as a nonlinear mixed integer optimization problem and solved by a particle swarm optimization algorithm. Sensitivity analysis to variation of the solver and parameter/parameter combination is further illustrated using several numerical example analyses. The main finding reveals that within a multinational supply chain system involving multi-retailers, an appreciation in the currency of the individual retailers' respective countries leads to decreases in their optimal order quantity and the manufacturer's optimal material purchase quantity. Furthermore, sensitivity analysis of retailer-related parameter combinations shows that the manufacturer's number of shipments is significantly influenced by changes in ordering cost, unit wholesale price, and exchange rate. Additionally, increased fixed cost parameters result in higher total carbon emissions, while heightened variable cost parameters lead to a reduction in total carbon emissions. Finally, the pursuit of net-zero carbon emissions is a crucial and publicly stated goal for enterprises, supply chains, and even governments. When it becomes necessary to make investments in carbon reduction, priority can be given to reducing the carbon emissions generated by the procurement, manufacturing, and delivery of finished products to achieve maximum benefits. In summary, the contributions of this study are well-positioned to provide valuable guidance to enterprises or supply chain decision-makers, especially those operating within a multinational framework. Its goal is to effectively balance carbon reduction and profitability within the context of global trends in carbon emission reduction. We anticipate that the findings will guide the enterprise or the supply chain toward sustainable development, aligning with the global trend of carbon emissions reduction.
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