Optimizing high-speed rail express vehicle-cargo matching under stochastic demands

LIU Kanglin; TENG Jinghong; MA Rui; SU Zimeng; LANG Maoxiang

doi:10.11860/j.issn.1673-0291.20240146

Abstract：To fully utilize the transport capacity of high-speed railways,improve operational effi-ciency,and enhance economic benefits,this study addresses the vehicle-cargo matching optimization problem in high-speed rail express freight services.The research comprehensively considers three transport modes,passenger-freight mixed operation,reserved carriages,and dedicated EMU trains,and four categories of goods:fresh produce,urgent documents,electronic products,and valuables.A two-stage stochastic programming model is proposed.In the first stage,train operation plans are deter-mined,including the selection of transport modes for each train and decisions on whether to dispatch dedicated EMU trains.In the second stage,based on the train plans from the first stage,optimal vehicle-cargo matching schemes are determined under various demand scenarios.To solve the model,a Mixed-Integer Programming-based Genetic Algorithm(MIP-GA)is designed.Finally,case studies are conducted using data from the Beijing-Shanghai high-speed railway,including four types of goods with stochastic demand,5 500 containers,and 24 trains comprising a total of 254 carriages.The model is validated through analysis of the value of stochastic solutions and further tested across 12 case sizes to evaluate algorithm performance.The results show that the stochastic programming model effec-tively manages demand fluctuations,reducing costs by 3.8%and increasing the on-time delivery rate by 13%compared to the average-demand model.The MIP-GA significantly accelerates computation,sav-ing an average of 75.88%in solving time,with an average optimality gap of only 0.20%compared to Gurobi,thereby enhancing computational efficiency without compromising solution quality.