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

Hsien-Pin Hsu, Tai-Lin Chiang, Chia-Nan Wang, Hsin-Pin Fu and Chien-Chang Chou
Additional contact information
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

Sustainability, 2019, vol. 11, issue 7, 1-21

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.

Keywords: berth allocation problem (BAP); quay crane assignment problem (QCAP); hybrid genetic algorithm (HGA); variable QC assignment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/7/2018/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/7/2018/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:7:p:2018-:d:220099

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().