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A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number

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  • 1College of Electronic Engineering, National University of Defense Technology, Hefei, China.

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Summary

This study introduces a new model for operational cooperative time scheduling in modern warfare, using an improved Bat algorithm to efficiently find optimal solutions for complex coordination challenges.

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Military Science

Background:

  • Modern warfare presents complex coordination challenges due to the comprehensive combat domain and intricate tasks.
  • Existing scheduling models struggle with the dynamic nature of combat coordinative time and task flexibility.

Purpose of the Study:

  • To develop a novel multi-objective operational cooperative time scheduling model accounting for time fluctuations and flexibility.
  • To propose an efficient algorithm for solving this complex optimization problem.

Main Methods:

  • Utilized improved triangular fuzzy numbers to represent combat mission time.
  • Developed a multi-objective operational cooperative time scheduling model.
  • Proposed the multi-objective improved Bat algorithm based on angle decomposition (MOIBA/AD) for optimization.
  • Enhanced MOIBA/AD with angle space decomposition and improved population replacement strategies.

Main Results:

  • The proposed MOIBA/AD algorithm effectively solves large-scale multi-objective combinatorial optimization problems.
  • MOIBA/AD demonstrated superior performance compared to MOBA, MOEA/D, NSGA-II, and MOPSO.
  • The method produced higher quality solutions with better Pareto solution set distribution.

Conclusions:

  • The developed MOIBA/AD algorithm offers a superior approach for operational cooperative time scheduling in complex environments.
  • The enhanced algorithm effectively addresses challenges in combat coordination time scheduling.