Optimizing Supply Chain Sustainability through AI-Driven Policies and Integrator Facility

Abstract

Supply chains play a pivotal role in shaping a nation's economic landscape, making their sustainability a paramount concern. However, there is a notable lack of comprehensive policy frameworks addressing this crucial issue. This research aims to fill this gap by introducing two novel policy approaches. Our study focuses on optimizing supply chain networks through the application of AI-driven policies. We analyze the effectiveness of two specific policies: one involving subsidies for suppliers and the other entailing government intervention via an integrator facility for packaging and coordination. To assess these policies, we develop mathematical models and optimize them using the Firefly Algorithm (FA). The research outcomes distinctly reveal that subsidies confer a discernible advantage upon the first model, underscoring their role in shaping its efficacy. Intriguingly, the second model emerges as a formidable contender, particularly when untethered from the support of subsidies. This illuminates the inherent robustness of the second model's design, standing resilient even without the crutch of financial incentives. Beyond the realm of subsidies, the research imparts a profound insight into the essence of holistic policy paradigms, underpinned by AI-driven methodologies. It champions the necessity for a comprehensive approach that extends beyond mere financial aid, advocating for the installation of regulatory frameworks that galvanize publishers' accountability. This multifaceted approach ensures that the trajectory of social welfare is seamlessly woven into the very fabric of the supply chain's functioning, securing a sustainable and equitable distribution of benefits.