Sustainable Supply Chain Optimization Using CRCTP and MCLP

The 21st century has seen a growing divide between urban and rural areas driven by urban development and migration from rural regions to cities. This shift, along with rising demand, has resulted in complex and unsustainable supply chains that significantly contribute to climate change. In response, many companies are prioritizing the development of more sustainable supply chains to meet customer demand. This paper aims to optimize supply chain logistics by selecting the best meeting points, locations, and vehicle capacities for various query points while fulfilling basic needs to deliver products to retailers at minimal cost. The study will utilize Collective Travel Planning alongside the Maximal Covering Location Problem (MCLP) to create a function capable of computing the most efficient route. This approach differs from previous methods by incorporating both product categories and vehicle capacities, factors that better reflect real-world conditions, including the dynamic fluctuations in supply and demand from both retailers and customers. The proposed function will be evaluated through experiments using synthetic data designed to model realistic-scale problems. The results of these evaluations will help assess the practical applicability and effectiveness of the developed function in optimizing supply chain routes, offering a more sustainable solution for supply chain management in the face of modern challenges.