Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.cie.2018.09.033
Title: Reactive strategy for discrete berth allocation and quay crane assignment problems under uncertainty
Authors: Xiang, Xi
Liu, Changchun 
Miao, Lixin
Keywords: Science & Technology
Technology
Computer Science, Interdisciplinary Applications
Engineering, Industrial
Computer Science
Engineering
Berth allocation
Quay crane assignment
Reactive strategy
Uncertainty
Prospect theory
DECISION-SUPPORT-SYSTEM
BI-OBJECTIVE MODEL
CONTAINER TERMINALS
SCHEDULING PROBLEM
NEIGHBORHOOD SEARCH
OPERATIONS-RESEARCH
ALGORITHM
TIME
OPTIMIZATION
HEURISTICS
Issue Date: 1-Dec-2018
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Citation: Xiang, 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
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.
Source Title: COMPUTERS & INDUSTRIAL ENGINEERING
URI: https://scholarbank.nus.edu.sg/handle/10635/155192
ISSN: 0360-8352
1879-0550
DOI: 10.1016/j.cie.2018.09.033
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