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Multi-objective bacterial colony optimization algorithm for integrated container terminal scheduling problem

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Abstract

This paper proposes a multi-objective integrated container terminal scheduling problem considering three key components: berth allocation, quay cranes assignment and containers transportation in port operation process. In the suggested problem, one of the objectives is to shorten service time of ships with by coordinating of quay cranes, and the other is to reduce operating costs of quay cranes and yard trucks. Then, a Multi-objective Bacterial Colony Optimization algorithm (MOBCO) incorporating concepts of multi-swarm, topology, personal best and global best, named Multi-objective BCO with ring topology (MORBCO), is designed to handle the resulting problem. The extension of standard MOBCO to the MORBCO involves the addition of three specialized strategies: global chemotaxis operation, elite reproduction strategy and personal best archive with neighborhood communication mechanism. In order to test the performance of the MORBCO, benchmark tests are performed and compared with traditional MOBCO and three other well-known multi-objective algorithms first. The computational results indicate that the proposed algorithm can outperform other rivals and efficiently solve a variety of multi-objective problems in most of cases. Subsequently, MORBCO and two best performing algorithms from the previous test are applied to three instances generated by the proposed model. Judging by quality and diversity of obtained non-dominant solutions, we find that MORBCO has superior performance, especially for large instances of the container terminal problem.

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (Grants Nos. 71571120, 71971143, 71901052), the Major Project for National Science Foundation of China (Grant No. 71790615), Department of Education of Guangdong Province (Grants Nos. 2018A073825, 2017GWTSCX038), Innovating and Upgrading Institute Project from Department of Education of Guangdong Province (Grant No. 2017GWTSCX038), Natural Science Foundation of Guangdong Province (Grant No. 2018A0303130055) and Soft Science Project of Guangdong Province. Lijing Tan and Li Li are corresponding authors.

Author information

Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Ben Niu, Qianying Liu & Li Li

  2. Institute of Big Data Intelligent Management and Decision, Shenzhen University, Shenzhen, 518060, China

    Ben Niu, Qianying Liu & Li Li

  3. Great Bay Area International Institute for Innovation, Shenzhen University, Shenzhen, 518060, China

    Ben Niu, Qianying Liu & Li Li

  4. School of Business Administration, Dongbei University of Finance and Economics, Dalian, 116025, China

    Zhengxu Wang

  5. College of Management, Shenzhen Institute of Information Technology, Shenzhen, China

    Lijing Tan

Authors
  1. Ben Niu

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  2. Qianying Liu

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  3. Zhengxu Wang

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  4. Lijing Tan

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  5. Li Li

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Correspondence toLijing Tan orLi Li.

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