Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.cie.2018.09.033
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dc.titleReactive strategy for discrete berth allocation and quay crane assignment problems under uncertainty
dc.contributor.authorXiang, Xi
dc.contributor.authorLiu, Changchun
dc.contributor.authorMiao, Lixin
dc.date.accessioned2019-06-06T01:43:25Z
dc.date.available2019-06-06T01:43:25Z
dc.date.issued2018-12-01
dc.identifier.citationXiang, Xi, Liu, Changchun, Miao, Lixin (2018-12-01). Reactive strategy for discrete berth allocation and quay crane assignment problems under uncertainty. COMPUTERS & INDUSTRIAL ENGINEERING 126 : 196-216. ScholarBank@NUS Repository. https://doi.org/10.1016/j.cie.2018.09.033
dc.identifier.issn0360-8352
dc.identifier.issn1879-0550
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/155192
dc.description.abstract© 2018 Elsevier Ltd This study examines the simultaneous allocation of berths and quay cranes under discrete berth situations with uncertainty at container terminals. First, a mixed-integer programming model that considers practical constraints with the objective of minimizing the baseline cost is formulated to obtain a baseline schedule. However, in reality, different disruptions, i.e., deviation of vessels’ arrival times, deviation of vessels’ loading and unloading operation times, calling of unscheduled vessels, and breakdown of quay cranes, will occur when executing the baseline schedule. Thereby, a reactive strategy, which takes the baseline schedule as a reference and aims to minimize the recovery cost, is proposed. Given that the cost value cannot simulate the choices of decision makers in reality, a behavior perception-based disruption model is proposed to effectively simulate a practical situation. A rolling horizon heuristic is presented to derive good feasible solutions. Computational tests are reported to show (i) the effectiveness of the proposed approach to solve a set of real instances to optimality; and (ii) the performance of proposed reactive strategy to conduct different disruptions; (iii) the comparisons between the proposed reactive strategy and proactive strategy.
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.sourceElements
dc.subjectScience & Technology
dc.subjectTechnology
dc.subjectComputer Science, Interdisciplinary Applications
dc.subjectEngineering, Industrial
dc.subjectComputer Science
dc.subjectEngineering
dc.subjectBerth allocation
dc.subjectQuay crane assignment
dc.subjectReactive strategy
dc.subjectUncertainty
dc.subjectProspect theory
dc.subjectDECISION-SUPPORT-SYSTEM
dc.subjectBI-OBJECTIVE MODEL
dc.subjectCONTAINER TERMINALS
dc.subjectSCHEDULING PROBLEM
dc.subjectNEIGHBORHOOD SEARCH
dc.subjectOPERATIONS-RESEARCH
dc.subjectALGORITHM
dc.subjectTIME
dc.subjectOPTIMIZATION
dc.subjectHEURISTICS
dc.typeArticle
dc.date.updated2019-06-03T08:12:10Z
dc.contributor.departmentINST OF OPERATIONS RESEARCH & ANALYTICS
dc.description.doi10.1016/j.cie.2018.09.033
dc.description.sourcetitleCOMPUTERS & INDUSTRIAL ENGINEERING
dc.description.volume126
dc.description.page196-216
dc.published.statePublished
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