Optimization of Integrated Passenger, Cargo, and Postal Service Scheduling Using Genetic Simulated Annealing Algorithm in Urban-Rural Public Transportation

This research paper introduces an innovative scheduling algorithm specifically designed for the integration of passenger, cargo, and postal operations within public transportation frameworks. The Genetic Simulated Annealing (GASA) algorithm is designed to address the complexity of these multifaceted service scheduling, ensuring a balance between passenger travel convenience, operational efficiency, and economic viability. The study focuses on the Xinle Special Route 1 urban-rural bus service in Shijiazhuang, China, and employs a dual-objective optimization model to minimize passenger travel time and maximize alliance profits. The results show that the proposed algorithm can significantly improve profitability by 66%, reduce the number of trips by 11%, and reduce operating costs by 7.5%, while maintaining passenger travel convenience. However, the study also acknowledges limitations, such as the need for more extensive data set validation, consideration of real-world traffic changes, and comprehensive sustainability assessments. This research offers a promising approach for optimizing passenger, cargo, and postal operation scheduling, with potential for further enhancements to strengthen its practical applicability and societal benefits.