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Research on Fuzzy Decision-Making Method of Task Allocation for Ship Multiagent Collaborative Design

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Listed:
  • Jinghua Li
  • Yiying Wang
  • Boxin Yang
  • Qinghua Zhou
  • Feihui Yuan
  • S. E. Najafi

Abstract

Since task allocation is one of the core tasks of ship design, the choice of its allocation strategy is a key factor that affects whether the task and the design agent can be beneficially matched. Different from the traditional one-way assignment mode of assigning tasks to designers, in the task assignment strategy of modern ship collaborative design mode, designers’ ability and benefit ratio is getting higher and higher. Therefore, in order to improve the efficiency and quality of task design, this paper proposes a multidesign agent-task allocation decision-making method. In this paper, the task attributes and designers’ attributes are introduced into the task allocation strategy model, and the fuzzy linguistic variable method is used to build the evaluation index matrix of the design agent, and the task timeliness function is established. Secondly, the multidesign agent-task benefit function is established and solved to obtain the best allocation strategy. Finally, through example verification and comparative analysis with the Round-Robin algorithm (RR) and the Weighted Round-Robin (WRR) algorithm, the validity, feasibility, and stability of the multidesign agent-task allocation decision-making method proposed in this paper are verified, and it is proved that the task allocation method takes the bilateral needs of the task and the design agent into account, solves the optimal allocation strategy of collaborative design tasks, and realizes the balanced allocation between the ship collaborative design task and the design agent.

Suggested Citation

  • Jinghua Li & Yiying Wang & Boxin Yang & Qinghua Zhou & Feihui Yuan & S. E. Najafi, 2022. "Research on Fuzzy Decision-Making Method of Task Allocation for Ship Multiagent Collaborative Design," Advances in Mathematical Physics, Hindawi, vol. 2022, pages 1-20, July.
  • Handle: RePEc:hin:jnlamp:6368110
    DOI: 10.1155/2022/6368110
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