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Multi-objective steel plate cutting optimization problem based on real number coding genetic algorithm.

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This study introduces an optimized rectangular packing method for steel plate cutting, achieving up to 92.73% raw material utilization. The novel algorithm significantly reduces waste and labor, enhancing efficiency in industrial applications.

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Area of Science:

  • Operations Research
  • Combinatorial Optimization
  • Industrial Engineering

Background:

  • The rectangular packing problem, an NP-complete challenge, is prevalent in industrial settings like steel plate cutting.
  • Current cutting schemes often lack global optimality, leading to material waste and increased processing costs.

Purpose of the Study:

  • To develop a generalized packing method for special-dimension rectangles.
  • To establish a multi-objective mixed-integer nonlinear programming model for optimizing steel plate cutting.
  • To design an efficient genetic algorithm for high-speed cutting scheme planning.

Main Methods:

  • Formulation of a multi-objective mixed-integer nonlinear programming model.
  • Development of a genetic algorithm tailored for the rectangular packing problem.
  • Application to a practical roll-fed disc shearing steel plate optimization scenario.

Main Results:

  • Achieved a raw material utilization rate of up to 92.73%.
  • Demonstrated significant reductions in labor requirements.
  • Validated the algorithm's ability to handle large data volumes efficiently.

Conclusions:

  • The proposed rectangular packing method offers strong universality and generalization capabilities.
  • The genetic algorithm provides a high-speed and efficient solution for complex cutting tasks.
  • The approach offers practical guidance for improving production and processing efficiency in industries.