Abstract
In today's economic globalization, the cold chain logistics industry is a fundamental sector supporting the development of the national economy, with sustainable development and a people-oriented approach being crucial. This paper investigates the path optimization problem of cold chain logistics vehicles that simultaneously pick up and deliver goods within a time window, aiming to achieve sustainable development in real-world cold chain logistics distribution. Additionally, the paper takes into account the varying speeds of vehicles over time to more accurately simulate real-world traffic conditions. The objective of the mathematical model is to minimize the total cost, which includes economic, environmental, social, and equity costs. To address this problem, a Heuristic Cross Brainstorm Optimization Algorithm (HCBSO) has been selected, and strategies such as segment-based initial solution construction and heuristic crossover have been designed to enhance the algorithm's performance. Furthermore, a departure time optimization strategy has been developed to avoid traffic congestion. The effectiveness of the model and algorithm has been verified through multiple sets of comparative tests. The test results indicate that the constructed model and designed algorithm can scientifically optimize departure times, plan vehicle routes to avoid traffic congestion, effectively reduce total costs, and promote the sustainable development of the cold chain logistics system.