Collaborative optimization of last-train timetables for metro network to increase service time for passengers

DOI10.1016/j.cor.2022.106091WikidataQ117042430 ScholiaQ117042430MaRDI QIDQ6109301

Ruihua Xu, Pengling Wang, Fangsheng Wang, Xuyang Song

Publication date: 4 July 2023

Published in: Computers \& Operations Research (Search for Journal in Brave)

zbMATH Keywords

genetic algorithmQ-learningcollaborative optimizationlast-train timetablelatest departure time

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Cites Work

• Variable and adaptive neighbourhood search algorithms for rail rapid transit timetabling problem
• \({\mathcal Q}\)-learning
• Synchronizing last trains of urban rail transit system to better serve passengers from late night trains of high-speed railway lines
• Railway timetabling with integrated passenger distribution
• A comparison of linear reformulations for multinomial logit choice probabilities in facility location models
• Strategic timetable scheduling for last trains in urban railway transit networks
• Scheduling local and express trains in suburban rail transit lines: mixed-integer nonlinear programming and adaptive genetic algorithm
• A Benders decomposition algorithm for demand-driven metro scheduling

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