现代制造工程 ›› 2025, Vol. 534 ›› Issue (3): 31-40.doi: 10.16731/j.cnki.1671-3133.2025.03.004

• 先进制造系统管理运作 • 上一篇    下一篇

考虑设备退化的离散车间生产排程优化研究*

王春1, 张朝阳1,2, 吉卫喜1,2, 卢璟钰1   

  1. 1 江南大学机械工程学院,无锡 214122;
    2 江苏省食品制造装备重点实验室,无锡 214122
  • 收稿日期:2024-07-23 发布日期:2025-03-28
  • 通讯作者: 张朝阳,博士,副教授,主要研究方向为智能制造系统与低碳制造。E-mail:cyzhang@jiangnan.edu.cn
  • 作者简介:王春,硕士研究生,主要研究方向为智能制造系统、智能设备运维。 E-mail:wangchun_327@163.com
  • 基金资助:
    *国家自然科学基金青年科学基金项目(51805213)

Research on optimization of production scheduling in discrete workshops considering equipment degradation

WANG Chun1, ZHANG Chaoyang1,2, JI Weixi1,2, LU Jingyu1   

  1. 1 School of Mechanical Engineering,Jiangnan University,Wuxi 214122,China;
    2 Jiangsu Provincial Key Laboratory of Food Manufacturing Equipment,Wuxi 214122,China
  • Received:2024-07-23 Published:2025-03-28

摘要: 在离散车间的生产环境中,设备退化过程会受到外部随机冲击的影响,生产计划的制定受设备状态影响。生产计划可以与维护计划进行联合优化,针对此提出一种考虑设备退化的生产排程优化模型。为求解该模型,采用一种考虑随机外部冲击的设备退化模型,并以最小化最大完工时间为目标,设计了一种三元进化遗传算法(TPEGA)。该算法采用功能不同的3个种群协作对最优解进行搜寻,在限制种群规模的同时持续保持优质解的多样性,为避免陷入局部最优,设计了局部最优概率模型与个体抛弃策略。同时为了优化初始种群,提出了一种综合考虑负载最大设备和工序加工时间的初始化贪心选择策略。实验结果表明了算法的有效性与模型的可行性。

关键词: 生产排程, 维护计划, 三元进化遗传算法, 设备退化

Abstract: In a discrete workshop production environment,the degradation process of equipment is influenced by external random shocks,and production planning is affected by equipment conditions. Production planning can be jointly optimized with maintenance scheduling. To address this,a production scheduling optimization model that considers equipment degradation was proposed. To solve this model,a degradation model considering random external shocks was used,with the goal of minimizing the maximum completion time. A Tri-Population Evolutionary Genetic Algorithm (TPEGA) was designed for this purpose. This algorithm employs three distinct populations with different functions to collaboratively search for the optimal solution,restricting the population size while maintaining diversity among high-quality solutions. To avoid falling into local optima,a local optima probability model and an individual discarding strategy were devised. Additionally,to optimize the initial population,a greedy selection strategy considering the equipment with the highest load and process time was proposed. Experimental results demonstrate the effectiveness of the algorithm and the feasibility of the model.

Key words: production scheduling, maintenance scheduling, Tri-Population Evolutionary Genetic Algorithm (TPEGA), equip-ment degradation

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