An intelligent green scheduling system for sustainable cold chain logistics

Abstract

This study proposes an intelligent green scheduling system for cold chain logistics (IGSS-CCL) to support the integration and coordination of resources. Post-COVID-19, the traditional cold product market is rapidly converting to retail stores and e-commerce portals owing to social distancing restrictions, which creates a requirement and opportunities for the development of cold chain logistics. However, urban governance requirements, such as pandemic prevention, traffic restriction, energy conservation, and emissions reduction, have added challenges to this development. Therefore, it is vital to design a cold chain logistics scheduling system that considers the economic, safety, and environmental factors. The proposed system includes three parts: (1) the framework structure of the cold chain logistics intelligent scheduling system; (2) a multi-objective scheduling optimization model to allow for efficient and dynamic coordination between the distribution, demand, and external environment; and (3) a two-stage optimization algorithm based on Dijkstra's algorithm and a non-dominated sorting genetic algorithm to support intelligent scheduling operations. Numerical experiments were conducted to analyze the performance of the proposed system and demonstrate its application. The results highlight that multi-objective tactical optimization in the IGSS-CCL is conducive to saving resources, protecting the environment, and promoting the sustainable development of cold chain logistics, which remains ahead of the traditional single-objective optimization method. Managers can use the suggested IGSS-CCL as a decision-support tool to control and supervise the scheduling operations of cold chain logistics.