A Bi-Population Co-Evolutionary Multi-Objective Optimization Algorithm for Production Scheduling Problems in a Metal Heat Treatment Process with Time Window Constraints

Jiahui Gu, Boheng Liu and Ziyan Zhao ()
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Jiahui Gu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Boheng Liu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Ziyan Zhao: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Mathematics, 2025, vol. 13, issue 16, 1-18

Abstract: Heat treatment is a critical intermediate process in copper strip manufacturing, where strips go through an air-cushion annealing furnace. The production scheduling for the air-cushion annealing furnace can contribute to cost reduction and efficiency enhancement throughout the overall copper strip production process. The production scheduling problem must account for time window constraints and gas atmosphere transition requirements among jobs, resulting in a complex combinatorial optimization problem that necessitates dual-objective optimization of the total atmosphere transition cost of annealing and the total penalties for time window violations. Most multi-objective optimization algorithms rely on the evolution of a single population, which makes them prone to premature convergence, leading to local optimal solutions and insufficient exploration of the solution space. To address the challenges above effectively, we propose a Bi-population Co-evolutionary Multi-objective Optimization Algorithm (BCMOA). Specifically, the BCMOA initially constructs two independent populations that evolve separately. When the iterative process meets predefined conditions, elite solution sets are extracted from each population for interaction, thereby generating new offspring individuals. Subsequently, these new offspring participate in elite solution selection alongside the parent populations via a non-dominated selection mechanism. The performance of the BCMOA has undergone extensive validation on benchmark datasets. The results show that the BCMOA outperforms its competitive peers in solving the relevant problem, thereby demonstrating significant application potential in industrial scenarios.

Keywords: multi-objective optimization algorithm; bi-population co-evolution; production scheduling; time window constraints (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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