4.7 Article

An integrated container terminal scheduling problem with different-berth sizes via multiobjective hydrologic cycle optimization

Journal

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Volume 37, Issue 12, Pages 11909-11925

Publisher

WILEY
DOI: 10.1002/int.23069

Keywords

evolutionary computing algorithm; hydrologic cycle optimization; integrated berth and quay crane allocation problem; multiobjective optimization; scheduling

Funding

  1. Natural Science Foundation of China [71971143, 62103286]
  2. Major Research Plan for National Natural Science Foundation of China [91846301]
  3. Major Project for National Natural Science Foundation of China [71790615]
  4. Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau [2019KZDXM030]
  5. Natural Science Foundation of Guangdong Province [2020A1515010749]
  6. Guangdong Province Innovation Team Intelligent Management and Interdisciplinary Innovation [2021WCXTD002]
  7. University of Macau [MYRG2019-00031FBA]
  8. Social Science Youth Foundation of Ministry of Education of China [21YJC630181]
  9. Basic and Applied Basic Research Foundation of Guangdong Province [2019A1515110401]

Ask authors/readers for more resources

This study investigates the integrated berth and quay crane allocation problem (BQCAP) and proposes a multiobjective model to solve these problems. The proposed algorithm is applied to a real-world case and outperforms other existing algorithms in solving BQCAP.
Integrated berth and quay crane allocation problem (BQCAP) are two essential seaside operational problems in container terminal scheduling. Most existing works consider only one objective on operation and partition of quay into berths of the same lengths. In this study, BQCAP is modeled in a multiobjective setting that aims to minimize total equipment used and overall operational time and the quay is partitioned into berths of different lengths, to make the model practical in the real-world and complex quay layout setting. To solve the new BQCAP efficiently, a multiobjective hydrologic cycle optimization algorithm is devised considering problem characteristics and historical Pareto-optimal solutions. Specifically, the quay crane of the large vessel in all Pareto-optimal solutions is rearranged to increase the chance of finding a good solution. Besides, worse solutions are probabilistic retained to maintain diversity. The proposed algorithm is applied to a real-world terminal scheduling problem with different sizes from a container terminal company. Experimental results show that our algorithm generally outperforms the other well-known peer algorithms and its variants on solving BQCAP, especially in finding the Pareto-optimal solutions range.

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