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A Discrete Brain Storm Optimization Algorithm for Hybrid Flowshop Scheduling Problems with Batch Production at Last

Kunkun Peng1,2, Chunjiang Zhang2, Weiming Shen2,3

  • 1School of Management, Wuhan University of Science and Technology, Wuhan 430065, China.

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|November 27, 2024
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Summary
This summary is machine-generated.

This study introduces a Discrete Brain Storm Optimization (DBSO) algorithm to solve the complex scheduling problems in steelmaking-refining-continuous casting (SRCC) processes. The DBSO algorithm efficiently optimizes production for enhanced efficiency and energy savings in the iron and steel industry.

Keywords:
batch productionbrain storm optimizationenergy savinghybrid flowshop schedulingiron and steelsteelmaking-refining-continuous casting

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Area of Science:

  • Industrial Engineering
  • Operations Research
  • Materials Science

Background:

  • The iron and steel industry is highly energy-intensive, facing significant challenges in production efficiency and resource management.
  • Steelmaking-refining-continuous casting (SRCC) represents a critical bottleneck in iron and steel production, with scheduling problems being NP-hard.
  • Optimizing SRCC processes is crucial for enhancing enterprise efficiency and achieving substantial energy and resource savings.

Purpose of the Study:

  • To address the NP-hard scheduling problems in the Steelmaking-Refining-Continuous Casting (SRCC) process.
  • To develop an efficient optimization algorithm for SRCC scheduling.
  • To improve production efficiency and reduce energy consumption in the iron and steel industry.

Main Methods:

  • Modeling SRCC scheduling as a hybrid flowshop problem with batch production.
  • Proposing a Discrete Brain Storm Optimization (DBSO) algorithm.
  • Designing specialized population initialization, cluster center replacement, and perturbation operators within the DBSO framework.

Main Results:

  • The proposed DBSO algorithm demonstrates enhanced intensification and diversification abilities.
  • A novel individual generation operator simultaneously improves both intensification and diversification.
  • Experimental results validate the efficiency of the DBSO algorithm for SRCC scheduling problems.

Conclusions:

  • The Discrete Brain Storm Optimization (DBSO) algorithm is an effective method for solving complex SRCC scheduling problems.
  • The enhancements in DBSO contribute to better process control and optimization in the iron and steel sector.
  • Optimized SRCC scheduling leads to significant improvements in production efficiency and energy conservation.