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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 |
Appears in Collections: | Staff Publications Elements |
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