考虑设备故障的铝卷批处理可重入两阶段协同调度问题

Collaborative Two-Stage Reentrant Batch Scheduling Problem for Aluminum Coils Considering Equipment Failures

摘要

针对铝卷冷轧和退火阶段设备故障导致的预调度方案失效问题，建立数学模型，提出基于种群状态自适应调节的开普勒优化算法 (Kepler Optimization Algorithm with Adaptive Adjustment Based on Population State, KOABPS)，设计了基于种群状态的自适应调节机制，使得算法具备感知搜索环境、动态调整行为的自主决策能力。在工厂数据集上进行了不同重调度方法的比较，并通过消融实验验证了 KOABPS 算法的有效性。为更深入研究算法性能，设计不同规模的随机实验，并将 KOABPS 算法与其他优化算法进行对比分析，结果表明该算法在求解质量和收敛速度方面均具有优势。

Abstract

Equipment failures in aluminum coil production disrupt normal manufacturing operations, and how to adjust scheduling schemes dynamically in response to such failures has become a critical practical issue. To address this problem, this paper establishes a corresponding mathematical model and proposes a Kepler Optimization Algorithm with Adaptive Adjustment Based on Population State (KOABPS). Specifically, a hierarchy-based dynamic decoding method is constructed, Latin hypercube sampling is adopted for population initialization to ensure sample diversity, a population state-based adaptive adjustment mechanism is designed, and Levy flight is introduced to help the algorithm escape local optima effectively. To verify the effectiveness of KOABPS, a factory dataset is built using actual production data from enterprises, and random datasets of different scales are further constructed according to the characteristics of practical production data. Comparisons between partial rescheduling and complete rescheduling are conducted on the factory dataset, and ablation experiments are carried out to demonstrate the validity of the proposed key modules in KOABPS. In addition, small-, medium- and large-scale random experiments are designed to compare KOABPS with other typical optimization algorithms. The results show that the proposed algorithm outperforms its counterparts in both solution quality and convergence speed.

关键词

铝卷生产 / 批处理 / 可重入调度 / 设备故障 / 开普勒优化算法 (KOA)

Key words

aluminum coil production / batch processing / reentrant scheduling / equipment failure / Kepler Optimization Algorithm (KOA)

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基金资助

国家自然科学基金(62076095)