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A hybrid genetic algorithm for the optimal transporter management plan in a shipyard

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(12), pp.49-56
  • DOI : 10.9708/jksci.2023.28.12.049
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 26, 2023
  • Accepted : November 29, 2023
  • Published : December 29, 2023

Jun-Ho Park 1 Yung-Keun Kwon 1

1울산대학교

Accredited

ABSTRACT

In this study, we propose a genetic algorithm (GA) to optimize the allocation and operation order of transporters. The solution in the GA is represented by a set of lists each of which the operation order of the corresponding transporter. In addition, it was implemented in the form of a hybrid genetic algorithm combining effective local search operations for performance improvement. The local search reduces the number of operating transporters by moving blocks from a transporter with a low workload into that with a high workload. To evaluate the effectiveness of the proposed algorithm, it was compared with Multi-Start and a pure genetic algorithm through a simulation environment similar in scale to an actual shipyard. For the largest problem, compared to them, the number of transporters was reduced by 40% and 34%, and the total task time was reduced by 27% and 17%, respectively.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.