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A Hybrid GA with Variable Quay Crane Assignment for Solving Berth Allocation Problem and Quay Crane Assignment Problem Simultaneously

Author

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  • Hsien-Pin Hsu

    (Department of Supply Chain Management, National Kaohsiung University of Science and Technology, Kaohsiung 81157, Taiwan)

  • Tai-Lin Chiang

    (Department of Business Administration, Minghsin University of Science and Technology, Xinfeng Hsinchu 30401, Taiwan)

  • Chia-Nan Wang

    (Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 83158, Taiwan)

  • Hsin-Pin Fu

    (Department of Marketing and Distribution, National Kaohsiung University of Science and Technology, Kaohsiung 81164, Taiwan)

  • Chien-Chang Chou

    (Department of Shipping Technology, National Kaohsiung University of Science and Technology, Kaohsiung 80543, Taiwan)

Abstract

Container terminals help countries to sustain their economic development. Improving the operational efficiency in a container terminal is important. In past research, genetic algorithms (GAs) have been widely used to cope with seaside operational problems, including the berth allocation problem (BAP) and quay crane assignment problem (QCAP) individually or simultaneously. However, most GA approaches in past studies were dedicated to generate time-invariant QC assignment that does not adjust QCs assigned to a ship. This may underutilize available QC capacity. In this research, three hybrid GAs (HGAs) have been proposed to deal with the dynamic and discrete BAP (DDBAP) and the dynamic QCAP (DQCAP) simultaneously. The three HGAs supports variable QC assignment in which QCs assigned to a ship can be further adjusted. The three HGAs employ the same crossover operator but a different mutation operator and a two-stage procedure is used. In the first stage, these HGAs can generate a BAP solution and a QCAP solution that is time-invariant. The time-invariant QC assignment solution is then further transformed into a variable one in the second stage. Experiments have been conducted to investigate the effects of the three HGA and the results showed that these HGAs outperformed traditional GAs in terms of fitness value. In particular, the HGA3 with Thoros mutation operator had the best performance.

Suggested Citation

  • Hsien-Pin Hsu & Tai-Lin Chiang & Chia-Nan Wang & Hsin-Pin Fu & Chien-Chang Chou, 2019. "A Hybrid GA with Variable Quay Crane Assignment for Solving Berth Allocation Problem and Quay Crane Assignment Problem Simultaneously," Sustainability, MDPI, vol. 11(7), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2018-:d:220099
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    References listed on IDEAS

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    1. Hsien-Pin Hsu & Chia-Nan Wang, 2020. "Resources Planning for Container Terminal in a Maritime Supply Chain Using Multiple Particle Swarms Optimization (MPSO)," Mathematics, MDPI, vol. 8(5), pages 1-31, May.
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    3. Shaojian Qu & Xinqi Li & Chang Liu & Xufeng Tang & Zhisheng Peng & Ying Ji, 2023. "Two-Stage Robust Programming Modeling for Continuous Berth Allocation with Uncertain Vessel Arrival Time," Sustainability, MDPI, vol. 15(13), pages 1-30, July.

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