Genetic Algorithm-Enhanced Cooperative Subway Transportation for Passengers and Cargo

This paper investigates the passenger and cargo common line model in subway freight transport with the objective of enhancing operational efficiency and cost reduction, ultimately minimizing the total system expenditure. It endeavors to establish a multi-factor model that encompasses the collaborative transportation of both subway passengers and cargo. To tackle this challenge, a genetic algorithm is applied and practically executed using Python. A specific subway line within an urban area is selected as the case study for in-depth optimization analysis. The results unequivocally demonstrate the algorithm’s effectiveness in substantially reducing the overall cost of collaborative subway passenger and cargo transportation. These findings provide compelling evidence for the practical feasibility of the optimization approach proposed in this paper across various operational scenarios.