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Research on hot rolling scheduling problem based on Two-phase Pareto algorithm.

Wang Chen1, Zhang Xiufeng1, Zhao Guohua1

  • 1Department of Mechanical Engineering, Hubei University of automotive technology, Shiyan, China.

Plos One
|December 28, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces a new model and algorithm for energy-saving hot rolling batch scheduling in steel production. The developed two-phase Pareto search algorithm (2PPLS) effectively minimizes energy consumption while meeting operational constraints.

Area of Science:

  • Operations Research
  • Industrial Engineering
  • Materials Science

Background:

  • Excess capacity and energy saving initiatives in the iron and steel industry present significant scheduling challenges.
  • Hot rolling batch scheduling is a complex, multi-objective, and multi-constraint optimization problem.

Purpose of the Study:

  • To develop an optimization model for energy-saving hot rolling batch scheduling.
  • To minimize energy consumption, adhere to process rules, and maximize resource utilization.
  • To introduce a novel algorithm for solving the proposed optimization model.

Main Methods:

  • A hybrid multi-objective prize-collecting vehicle routing problem (HPCVRP) model was formulated.
  • A two-phase Pareto search algorithm (2PPLS) was designed, incorporating MOEA/D-PBI and MOACS-PLS.

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  • The algorithm was validated through simulation experiments and applied to real steel plant data.

    • Main Results:

    • The 2PPLS algorithm demonstrated superior performance compared to five other algorithms in simulations.
    • Application to actual slab data from a Shanghai steel plant confirmed effectiveness.
    • Significant reductions in energy consumption for hot rolling batch scheduling were achieved.

    Conclusions:

    • The proposed HPCVRP model and 2PPLS algorithm provide an effective solution for energy-saving hot rolling batch scheduling.
    • The approach successfully balances energy reduction with operational requirements and resource maximization.
    • This research offers practical benefits for the steel industry's sustainability goals.