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A multi-objective genetic algorithm for yard crane scheduling problem with multiple work lines

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Abstract

Due to increasing ships and quay cranes, container terminals operations become more and more busy. The traditional handling based on work line is converted into pool strategy, namely loading and unloading containers with multiple work lines are operating simultaneously. In the paper we discuss the yard crane scheduling problem with multiple work lines in container terminals. We develop a multi-objective 0-1 integer programming model considering the minimum total completion time of all yard cranes and the maximization balanced distribution of the completion time at the same time. With the application of adaptive weight GA approach, the problem can be solved by a multi-objective hybrid genetic algorithm and the Pareto solutions can be finally got. Using the compromised approach, the nearest feasible solution to ideal solution is chosen to be the best compromised Pareto optimal solution of the multi-objective model. The numerical example proves the applicability and effectiveness of the proposed method to the multi-objective yard crane scheduling problem.

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Acknowledgments

This research is partly supported by National Natural Science Foundation of China (71071093), partly Shanghai Municipal Natural Science Foundation (10ZR1413300), Innovation Program of Shanghai Municipal Education Commission (11YZ136), Foundation of Shanghai Maritime University (s2009286), Science and Technology Commission Foundation of Shanghai (08170511300, 09DZ2250400, 9530708200, 10190502500), Shanghai Education Commission Leading Academic Discipline Project (J50604), the Grant-in-Aid for Scientific Research (C) of Japan Society of Promotion of Science (JSPS) No. 245102190001, National Tsing Hua University (NSC 101-2811-E-007-004, NSC 102-2811-E-007-005) and the Dongseo Frontier Project Research Fund of Dongseo University in 2011.

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Authors and Affiliations

  1. Logistics Research Center, Shanghai Maritime University, Shanghai, China

    Cheng-Ji Liang & Min Chen

  2. Fuzzy Logic Systems Institute, Iizuka City, Japan

    Mitsuo Gen

  3. National Tsing Hua University, Hsinchu City, Taiwan

    Mitsuo Gen

  4. Division of Computer and Information Engineering, Dongseo University, Busan, Korea

    Jungbok Jo

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  1. Cheng-Ji Liang
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  2. Min Chen
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  3. Mitsuo Gen
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  4. Jungbok Jo
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Correspondence to Jungbok Jo.

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Liang, CJ., Chen, M., Gen, M. et al. A multi-objective genetic algorithm for yard crane scheduling problem with multiple work lines. J Intell Manuf 25, 1013–1024 (2014). https://doi.org/10.1007/s10845-013-0792-4

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  • DOI: https://doi.org/10.1007/s10845-013-0792-4

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