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

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.