Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31319
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dc.contributor.authorShen, Yindongen_UK
dc.contributor.authorPeng, Kunkunen_UK
dc.contributor.authorChen, Kaien_UK
dc.contributor.authorLi, Jingpengen_UK
dc.date.accessioned2020-06-20T00:13:11Z-
dc.date.available2020-06-20T00:13:11Z-
dc.date.issued2013-10en_UK
dc.identifier.urihttp://hdl.handle.net/1893/31319-
dc.description.abstractThis paper presents an adaptive evolutionary approach incorporating a hybrid genetic algorithm (GA) for public transport crew scheduling problems, which are well-known to be NP-hard. To ensure the search efficiency, a suitable chromosome representation has to be determined first. Unlike a canonical GA for crew scheduling where the chromosome length is fixed, the chromosome length in the proposed approach may vary adaptively during the iterative process, and its initial value is elaborately designated as the lower bound of the number of shifts to be used in an unachievable optimal solution. Next, the hybrid GA with such a short chromosome length is employed to find a feasible schedule. During the GA process, the adaptation on chromosome lengths is achieved by genetic operations of crossover and mutation with removal and replenishment strategies aided by a simple greedy algorithm. If a feasible schedule cannot be found when the GA’s termination condition is met, the GA will restart with one more gene added. The above process is repeated until a feasible solution is found. Computational experiments based on 11 real-world crew scheduling problems in China show that, compared to a fuzzy GA known to be well performed for crew scheduling, better solutions are found for all the testing problems. Moreover, the algorithm works fast, has achieved results close to the lower bounds obtained by a standard linear programming solver in terms of the number of shifts, and has much potential for future developments.en_UK
dc.language.isoenen_UK
dc.publisherElsevier BVen_UK
dc.relationShen Y, Peng K, Chen K & Li J (2013) Evolutionary crew scheduling with adaptive chromosomes. Transportation Research Part B: Methodological, 56, pp. 174-185. https://doi.org/10.1016/j.trb.2013.08.003en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectCrew schedulingen_UK
dc.subjectMetaheuristicen_UK
dc.subjectGenetic algorithmen_UK
dc.subjectAdaptive chromosomeen_UK
dc.subjectPublic transiten_UK
dc.titleEvolutionary crew scheduling with adaptive chromosomesen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2999-12-31en_UK
dc.rights.embargoreason[1-s2.0-S0191261513001392-main.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1016/j.trb.2013.08.003en_UK
dc.citation.jtitleTransportation Research Part B: Methodologicalen_UK
dc.citation.issn0191-2615en_UK
dc.citation.issn0191-2615en_UK
dc.citation.volume56en_UK
dc.citation.spage174en_UK
dc.citation.epage185en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailjli@cs.stir.ac.uken_UK
dc.citation.date02/09/2013en_UK
dc.contributor.affiliationHuazhong University of Science and Technologyen_UK
dc.contributor.affiliationHuazhong University of Science and Technologyen_UK
dc.contributor.affiliationHuazhong University of Science and Technologyen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000325840100011en_UK
dc.identifier.scopusid2-s2.0-84883342429en_UK
dc.identifier.wtid1454945en_UK
dc.contributor.orcid0000-0002-6758-0084en_UK
dc.date.accepted2013-08-09en_UK
dc.date.filedepositdate2020-06-19en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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