Optimizing Sustainable Supply Chain Resource Integration: A Multi-Objective Approach Considering Consumer Green Preferences and Policy Interventions

In the context of escalating environmental awareness and the rise of green consumer preferences, enterprises are confronted with the complex challenge of aligning supply and demand while maintaining the quality and long-term value of sustainable supply chain (SSC) resource integration. This study introduces consumer green preference as a pivotal moderating factor that influences demand variability across supply chain networks. To address this challenge, a multi-objective optimization model is proposed, designed to simultaneously maximize supply-demand matching utility, enhance the quality of SSC resource integration, and control the cost of the supply chain. The model also incorporates the dual impact of policy interventions on both consumer behavior and enterprise operations, thereby offering a comprehensive framework for improving SSC sustainability. The optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which yields Pareto-optimal solutions that balance the competing objectives. A numerical case study is presented to demonstrate the feasibility and practical applicability of the proposed model. This research contributes to the literature by integrating consumer behavior and policy factors into the design of SSCs. Specifically, the numerical case demonstrates that targeted policy interventions can enhance supply-demand matching utility while reducing integration costs, thereby providing actionable insights for organizations aiming to achieve sustainability through enhanced resource integration strategies and long-term value optimization.