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Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem
Normandie Univ, UNIHAVRE, Le Havre, 76600, France.
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2019 (English)In: International Journal of Industrial Engineering Computations, ISSN 1923-2926, E-ISSN 1923-2934, Vol. 10, no 1, p. 111-132Article in journal (Refereed) Published
Abstract [en]

This work is devoted to the study of the Uncertain Quay Crane Scheduling Problem (QCSP), where the loading /unloading times of containers and travel time of quay cranes are considered uncertain. The problem is solved with a Simulation Optimization approach which takes advantage of the great possibilities offered by the simulation to model the real details of the problem and the capacity of the optimization to find solutions with good quality. An Ant Colony Optimization (ACO) meta-heuristic hybridized with a Variable Neighborhood Descent (VND) local search is proposed to determine the assignments of tasks to quay cranes and the sequences of executions of tasks on each crane. Simulation is used inside the optimization algorithm to generate scenarios in agreement with the probabilities of the distributions of the uncertain parameters, thus, we carry out stochastic evaluations of the solutions found by each ant. The proposed optimization algorithm is tested first for the deterministic case on several well-known benchmark instances. Then, in the stochastic case, since no other work studied exactly the same problem with the same assumptions, the Simulation Optimization approach is compared with the deterministic version. The experimental results show that the optimization algorithm is competitive as compared to the existing methods and that the solutions found by the Simulation Optimization approach are more robust than those found by the optimization algorithm.

Place, publisher, year, edition, pages
Growing Science , 2019. Vol. 10, no 1, p. 111-132
Keywords [en]
Container terminal, Simulation Optimization, Quay crane, Uncertainty
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-260263DOI: 10.5267/j.ijiec.2018.2.002ISI: 000468073300007Scopus ID: 2-s2.0-85048278088OAI: oai:DiVA.org:kth-260263DiVA, id: diva2:1355032
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QC 20191113

Available from: 2019-09-26 Created: 2019-09-26 Last updated: 2019-11-13Bibliographically approved

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Abourraja, Mohamed Nezar
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
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