考虑变工艺参数的柔性作业车间动态调度

doi:10.16731/j.cnki.1671-3133.2026.02.002

摘要/Abstract

摘要： 针对柔性作业车间加工过程中出现的各种订单扰动和机器故障问题,在工艺参数可变的条件下,以完工时间和能耗为优化目标,建立变工艺参数动态调度模型,并提出一种自适应动态调度策略来保证调度稳定性。为更好地发挥柔性作业车间的调度优化潜力,考虑加工工艺参数与车间调度的联系,提出扰动事件下机器加工工艺参数与动态调度相结合的综合优化方法,并提出一种改进灰狼算法求解。算法根据集成优化问题的特点,采用三层编码的方法,使得该算法可优化工序的工艺参数。针对集成优化问题的三层编码方式,提出了一种机床选择序列和工艺参数模式选择序列更新策略,引导算法向Pareto前沿靠近。最后通过生产实例和对比实验,验证所提方法的有效性和优越性。

关键词: 扰动事件, 动态调度, 工艺参数, 柔性作业车间, 灰狼算法

Abstract: In response to various order disturbances and machine failure issues in the flexible job shop processing, under the condition of variable process parameters model is established with completion time and energy consumption as the optimization objectives. Additionally, an adaptive dynamic scheduling strategy is proposed to ensure scheduling stability. To fully exploit the scheduling optimization potential of flexible job shops, it considers the relationship between processing parameters and workshop scheduling. It proposes a comprehensive optimization method that combines machine processing parameters with dynamic scheduling under perturbation events. An improved grey wolf algorithm is introduced for solving this method. The algorithm adopts a three-layer encoding method based on the characteristics of integrated optimization problems, allowing the algorithm to optimize the process parameters of the process. To address the three-layer encoding method for integrated optimization problems, a strategy for updating the machine selection sequence and the process parameter mode selection sequence is proposed to guide the algorithm towards the Pareto frontier. Finally, the effectiveness and superiority of the proposed method are verified through production instances and comparative experiments.

Key words: disturbance events, dynamic scheduling, process parameters, flexible job shop, grey wolf algorithm

中图分类号:

TH186

李浩平, 李景瑞, 杜昕毅, 金朱鸿, 于波涛. 考虑变工艺参数的柔性作业车间动态调度[J]. 现代制造工程, 2026, 545(2): 12-23.

LI Haoping, LI Jingrui, DU Xinyi, JIN Zhuhong, YU Botao. Flexible job shop dynamic scheduling considering variable process parameters[J]. Modern Manufacturing Engineering, 2026, 545(2): 12-23.

参考文献

[1] 詹欣隆,张超勇,孟磊磊,等. 面向高效低碳的切削参数与柔性作业车间调度集成建模与优化[J]. 计算机集成制造系统,2021,27(12):3519-3535.
[2] ZHANG Y,LIU Q,ZHOU Y,et al. Integrated Optimization of Cutting Parameters and Scheduling for Reducing Carbon Emissions[J]. Journal of Mechanical Engineering,2017,149:886-895.
[3] 刘琼,周迎冬,张漪. 面向低碳的切削参数与调度集成优化[J].机械工程学报,2017,53(5):24-33.
[4] 朱赟海,罗陆锋,卢清华,等. 面向订单扰动的柔性作业车间动态调度方法[J/OL]. 计算机集成制造系统:1-23[2024-03-01].https://kns.cnki.net/kcms/detail//11.5946.TP.20231127.1519.008.html.
[5] BAYKASOGLU A,MADENOGLU F S. Greedy randomized adaptive search procedure for simultaneous scheduling of production and preventive ma intenance activities in dynamic flexible job shops[J]. Soft Computing,2021,25(23):14893-14932.
[6] 李聪波,寇阳,雷焱绯,等. 基于动态事件的柔性作业车间重调度节能优化[J]. 计算机集成制造系统,2020,26(2):288-299.
[7] 袁绩乾,李文贵. 机械制造技术基础[M]. 北京:机械工业出版社,2001.
[8] 朱传军,邱文,张超勇,等. 多目标柔性作业车间稳健性动态调度研究[J]. 中国机械工程,2017,28(2):173-182.
[9] 顾九春,姜天华,朱惠琦. 多目标离散灰狼优化算法求解作业车间节能调度问题[J]. 计算机集成制造系统,2021,27(8): 2295-2306.
[10] 詹欣隆,张超勇,孟磊磊,等. 基于改进引力搜索算法的铣削加工参数低碳建模及优化[J]. 中国机械工程,2020,31(12): 1481-1491.
[11] 李聪波,刘飞,曹华军. 机械加工制造系统能效理论与技术[M]. 北京:机械工业出版社,2022.
[12] 汪双喜,张超勇,刘琼,等. 不同再调度周期下的柔性作业车间动态调度[J]. 计算机集成制造系统,2014,20(10):2470-2478.
[13] SHAHGHOLI ZADEH M,KATEBI Y,DONIAVI A. A heuristic model for dynamic flexible job shop scheduli ng problem considering variable processing times[J].International Journal of Production Research,2018,56(24):1-16.
[14] 吕岩,徐正军,李聪波,等. 考虑扰动事件的机械加工工艺参数与车间动态调度综合节能优化[J]. 机械工程学报, 2022,58(19):242-255.
[15] LI C,LI L,TANG Y,et al. A comprehensive approach to parameters optimization of energy-aware CNC milling[J]. Journal of Intelligent Manufacturing,2019,30:123-138.
[16] LI L, LI C, TANG Y, et al. An Integrated Solution to Minimize the Energy Consumption of a Resource-Constr ained Machining System[J]. IEEE Transactions on Autom ation Science and Engineering,2020, 17(3):1158-1175.
[17] 吕岩. 扰动事件下柔性作业车间工艺规划与动态调度集成节能优化研究[D]. 重庆:重庆大学,2022.
[18] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software,2014,69(3):46-61.
[19] 顾九春,姜天华,朱惠琦. 多目标离散灰狼优化算法求解作业车间节能调度问题[J]. 计算机集成制造系统,2021,27(8):2295-2306.
[20] 伍铁斌,桂卫华,阳春华,等. 用对数函数描述收敛因子的改进灰狼优化算法及其应用[J]. 中南大学学报(自然科学版),2018,49(4):857-864.