Analysis of Research Progress on Inventory Optimization in Green Supply Chain

Abstract

Against the backdrop of the "dual carbon" strategy and global sustainable development goals, optimizing green supply chain inventory has gradually become an important research topic in both academia and industry. This article provides a systematic review and overview of relevant research in the past five years. Research has found that traditional inventory optimization models have gradually expanded from a single cost minimization objective to simultaneously consider multidimensional objectives such as carbon emissions and service levels. In terms of methodology, mathematical programming, intelligent optimization algorithms, and simulation have been widely applied, and the introduction of uncertainty handling, policies, and financial factors has made the model more practical. In terms of industry applications, the manufacturing industry focuses on closed-loop inventory and recycling strategies, while the e-commerce and fast-moving consumer goods industries rely on the Internet of Things, big data, and dynamic replenishment technology to achieve high turnover and low-carbon operations. Despite some progress, existing research still faces issues such as inconsistent carbon accounting standards, lack of consideration for social performance and institutional implementation costs, and incomplete incentive mechanisms upstream and downstream. Based on this, this article proposes that future research should strengthen interdisciplinary integration and real data verification, explore supply chain collaboration mechanisms, interpretability of deep optimization, and effective integration of policies and practices, in order to promote the further development of green supply chain management theory and practice.