Advanced Optimization for Enhancing Sustainability in Metropolitan Cold Chain Systems

The objective of this study is to explore the cold chain system in a metropolitan area, focusing on the overall system cost encompassing both distribution centers and transportation. The research delves into the planning of urban cold chain systems, considering fluctuating minimum customer demands, the traffic conditions of potential new centers, and the variability in carbon-trading prices. To manage the complexity of these objectives and inherent uncertainties, we introduce a flexible chance-constrained programming model for the cold chain system (FCCP-CCS). An FCCP-CCS programming model is developed to address the multifaceted goals and various uncertainties. The effectiveness of this model is validated through experimental analysis using real-world data from a major city’s cold chain system. The findings of this study reveal several key insights: (1) The levels of confidence and satisfaction significantly impact system optimization, with higher levels leading to increased consumption. (2) Customer demand variations would determine the transportation and the potential new centers in the system. (3) The surroundings of a distribution center partly indicate its service quality. (4) Governmental adjustments in carbon-trading prices can effectively enhance the overall sustainability of the urban cold chain system. This research highlights the importance of optimization in designing and managing urban cold chain systems, particularly in environmental sustainability.