Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCC.2008.923872
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dc.titleTimetable synchronization of mass rapid transit system using multiobjective evolutionary approach
dc.contributor.authorKwan, C.M.
dc.contributor.authorChang, C.S.
dc.date.accessioned2014-10-07T04:38:36Z
dc.date.available2014-10-07T04:38:36Z
dc.date.issued2008
dc.identifier.citationKwan, C.M., Chang, C.S. (2008). Timetable synchronization of mass rapid transit system using multiobjective evolutionary approach. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 38 (5) : 636-648. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCC.2008.923872
dc.identifier.issn10946977
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83212
dc.description.abstractUsers of mass rapid transit are often required to make transfers between different train lines to reach their destinations. Timetable synchronization minimizes delays during such transfers. This paper formulates a novel measure for timetable synchronization by means of a total passenger dissatisfaction index (TPDI); and the impact of such synchronization on the original unsynchronized timetable is accounted using a total deviation index (TDV) that assigns penalties when deviations from the original timetable are incurred. Pareto fronts displaying the relationship between TPDI and TDV are generated using the state-of-the-art nondominated sorting genetic algorithm 2 (NSGA 2). To further improve NSGA 2, three schemes - the use of a variant of the NSGA2 with differential evolution, a process we termed "seeding," and finally a hybrid combination with local search techniques like heuristic hill climbing, tabu search, and simulated annealing - are proposed. Simulation results demonstrate that the "seeded" NSGA2-DE combined with the hill climbing heuristic produce the best results for the application. Solutions from the Pareto fronts are chosen for implementation to describe the different operating regions. A discussion section details the advantages and drawbacks of the proposed schemes. © 2008 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSMCC.2008.923872
dc.sourceScopus
dc.subjectDifferential evolution (DE)
dc.subjectHill climbing (HC)
dc.subjectMass rapid transit (MRT)
dc.subjectNondominated sorting genetic algorithm 2 (NSGA 2)
dc.subjectPareto-optimality
dc.subjectSimulated annealing (SA)
dc.subjectTabu search (TS)
dc.subjectTimetable synchronization
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TSMCC.2008.923872
dc.description.sourcetitleIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
dc.description.volume38
dc.description.issue5
dc.description.page636-648
dc.description.codenITCRF
dc.identifier.isiut000259192000002
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