考虑预保养的批量流柔性作业车间绿色调度

doi:10.3901/JME.260551

机械工程学报 ›› 2026, Vol. 62 ›› Issue (15): 244-261.doi: 10.3901/JME.260551

• 特邀专栏：大模型时代的智能设计与制造 • 上一篇

扫码分享

考虑预保养的批量流柔性作业车间绿色调度

王玉芳^1,2,3, 华晓麟¹, 章殿清¹, 刘平英¹

1. 南京信息工程大学自动化学院南京 210044;
2. 南京信息工程大学江苏省大气环境与装备技术协同创新中心南京 210044;
3. 南京信息工程大学江苏省气象能源利用与控制工程技术研究中心南京 210044

收稿日期:2025-08-29 修回日期:2026-01-23 发布日期:2026-10-13
作者简介:王玉芳，女，1979年出生，博士，副教授，硕士研究生导师。主要研究方向为车间调度，智能算法。E-mail：wangyufang@nuist.edu.cn;华晓麟，男，1999年出生，硕士研究生。主要研究方向为生产调度，进化算法。E-mail：202212490040@nuist.edu.cn;章殿清，男，1999年出生，硕士研究生。主要研究方向为绿色调度，强化学习算法。E-mail：842101808@qq.com;刘平英(通信作者)，1979年出生，硕士，实验师。主要研究方向为工业自动控制技术与算法。E-mail：lpy@nuist.edu.cn
基金资助:
国家自然科学基金资助项目(51705260)。

Lot Streaming Flexible Job-shop Scheduling with Pre-maintenance

WANG Yufang^1,2,3, HUA Xiaolin¹, ZHANG Dianqing¹, LIU Pingying¹

1. School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044;
2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044;
3. Jiangsu Engineering Research Center on Meteorological Energy Using and Control, Nanjing University of Information Science & Technology, Nanjing 210044

Received:2025-08-29 Revised:2026-01-23 Published:2026-10-13

摘要/Abstract

摘要： 考虑到实际生产中订单的批量性、加工模具的频繁切换、工件的运转需求和累计工时对机器能耗的综合影响，将多时间约束和预保养机制纳入批量调度中，构建以最小化完工时间和能耗为目标的批量流柔性作业车间绿色调度模型。通过对持续加工机器进行必要的预保养，有效减少累计工时对机器能耗的动态影响。针对批量调度和预保养特征，提出基于子批划分的Jaya算法，设计了多重优化机制。首先，引入多种基于子批划分的Jaya优化引导机制加快算法收敛速度，增加种群多样性，提高算法的全局搜索能力；其次，设计了基于子批划分的贪婪搜索策略，提高算法的局部寻优能力。最后，提出基于子批数量的禁忌搜索策略，提高算法的进化效率，减少冗余计算的同时对复杂的个体进行多次搜索，扩大搜索空间。通过消融实验验证所提算法的有效性，与其他4篇文献算法在13组拓展算例中进行了对比，验证了所提算法的优越性。

关键词: 柔性作业车间, 多时间约束, 批量调度, 预保养, 子批划分

Abstract: Considering the batch nature of production orders, the frequent need for mold changes during processing, the transportation requirements of workpieces, and the impact of cumulative processing hours on machine energy consumption, a green scheduling model for lot streaming flexible job-shop is developed. This model incorporates multiple temporal constraints and pre-maintenance mechanisms into batch scheduling with the dual objectives of minimizing makespan and energy consumption. By conducting necessary pre-maintenance on the continuous processing machines, the dynamic impact of cumulative working hours on the energy consumption of the machines can be effectively reduced. Based on the characteristics of batch scheduling and pre-maintenance, a Jaya algorithm based on sub-batch division is proposed, and multiple optimization mechanisms are designed. Firstly, various Jaya optimization guidance mechanisms based on sub-batch division are introduced to accelerate the convergence rate, increase population diversity, and enhance the global search capability of the algorithm. Secondly, a greedy search strategy based on sub-batch division is devised to improve the algorithm’s optimization ability and assist in escaping local optima. Finally, a tabu search strategy based on the number of sub-batches is proposed to improve the evolutionary efficiency of the algorithm, reduce redundant calculations, and enable multiple searches of complex individuals, thereby broadening the search space. The effectiveness of the proposed algorithm is confirmed through ablation experiments. Comparative studies with four other algorithms across 13 extended case examples demonstrate the superiority of the proposed approach.

Key words: flexible job-shop scheduling, multiple temporal constraints, batch scheduling, pre-maintenance, sub-batch partition

中图分类号:

TH165
TP18

王玉芳, 华晓麟, 章殿清, 刘平英. 考虑预保养的批量流柔性作业车间绿色调度[J]. 机械工程学报, 2026, 62(15): 244-261.

WANG Yufang, HUA Xiaolin, ZHANG Dianqing, LIU Pingying. Lot Streaming Flexible Job-shop Scheduling with Pre-maintenance[J]. Journal of Mechanical Engineering, 2026, 62(15): 244-261.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260551

http://www.cjmenet.com.cn/CN/Y2026/V62/I15/244

参考文献

[1] 梁志珍，王晓佳. 双资源约束的柔性作业车间鲁棒调度方法[J]. 机械工程学报，2024，60(6)：114-126. LIANG Zhizhen，WANG Xiaojia. Dual resource constraints flexible job shop robust scheduling method[J]. Journal of Mechanical Engineering，2024，60(6)：114-126.
[2] BRUCKER P, SCHLIE R. Job-shop scheduling with multi-purpose machines[J]. Computing，1990，45(4)：369-375.
[3] 闫富乾，陈浩杰，丁国富，等. 求解柔性分批调度问题的双层搜索框架入侵杂草算法[J]. 计算机集成制造系统，2023，29(2)：556-567. YAN Fuqing，CHEN Haojie，DING Guofu. Improved invasive weed algorithm based on double layer search framework for flexible batch scheduling problem[J]. Computer Integrated Manufacturing Systems，2023，29(2)：556-567.
[4] ABDOLLAHZADEH SANGROUDI H，RANJBAR- BOURANI M. Solving a flexible job shop lot sizing problem with shared operations using a self-adaptive COA[J]. International Journal of Production Research，2021，59(2)：483-515.
[5] JI B，ZHANG S J，YU S S，et al. Novel model and solution method for flexible job shop scheduling problem with batch processing machines[J]. Computers & Operations，2024，161：106442.
[6] YUNUSOGLU P，TOPALOGLUYILDIZ S. Solving the flexible job shop scheduling and lot streaming problem with setup and transport resource constraints[J]. International Journal of Systems Science，2023，54(11)： 2443.
[7] LI X，LU J，YANG C，et al. Research of flexible assembly job-shop batch-scheduling problem based on improved artificial bee colony[J]. Frontiers in Bioengineering and Biotechnology，2022，10：909548.
[8] XIE F，LI L，LI L，et al. A decomposition-based multi-objective Jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting[J]. Expert Systems with Applications，2023，228： 120402.
[9] 王建华，潘宇杰，孙瑞. 考虑机床折旧的柔性作业车间绿色调度算法[J]. 计算机应用，2020，40(1)：43-49. WANG Jianhua，PAN Yujie，SUN Rui. Flexible job-shop green scheduling algorithm considering machine tool depreciation[J]. Journal of Computer Applications，2020，40(1)：43-49.
[10] YANG B，LIAW P K，MORRISON M，et al. Temperature evolution during fatigue damage[J]. Intermetallics，2005，13(3)：419-428.
[11] YAN J，HUA D. Energy consumption modeling for machine tools after preventive maintenance[C]. 2010 IEEE International Conference on Industrial Engineering and Engineering Management，Macao，China. 2010： 2201-2205.
[12] RAO R. Jaya：A simple and new optimization algorithm for solving constrained and unconstrained optimization problems[J]. Engineering Computations，2016，7(1)：19-34.
[13] PAN Y，GAO K，LI Z，et al. Solving biobjective distributed flow-shop scheduling problems with lot-streaming using an improved jaya algorithm[J]. IEEE Transactions on Cybernetics，2023，53(6)：3818-3828.
[14] CALDEIRA R H，GNANAVELBABU A. A Pareto based discrete Jaya algorithm for multi-objective flexible job shop scheduling problem[J]. Expert Systems with Applications，2021，170：114567.
[15] BEREN G Y，OMER F Y. Lot streaming in hybrid flowshop scheduling problem by considering equal and consistent sublots under machine capability and limited waiting time constraint[J]. Computers & Industrial Engineering，2022，173：108745.
[16] HUANG R H，YU T H. An effective ant colony optimization algorithm for multi-objective job-shop scheduling with equal-size lot-splitting[J]. Applied Soft Computing，2017，57：642-656.
[17] 王玉芳，华晓麟，章殿清，等. 基于混合Jaya算法的多时间约束柔性作业车间节能调度[J]. 控制工程，2025，32(6)：1074-1085. WANG Yufang，HUA Xiaolin，ZHANG Dianqing，et al. Energy-efficient scheduling of flexible job-shop with multiple temporal constraints based on hybrid jaya algorithm[J]. Control Engineering of China，2025，32(6)： 1074-1085.
[18] ZHANG H L，XU G J，PAN R L，et al. A novel heuristic method for the energy-efficient flexible job-shop scheduling problem with sequence-dependent set-up and transportation time[J]. Engineering Optimization，2021，54(10)：1646-1667.
[19] SUN J H，ZHANG G H，LU J，et al. A hybrid many -objective problem with transportation and setup times[J]. Computers & Operations Research，2021，132： 105263.
[20] TIAN Y，CHENG R，ZHANG X Y，et al. PlatEMO：a MATLAB platform for evolutionary multi-objective optimization[J]. IEEE Computational Intelligence Magazine，2017，12(4)：73-87.
[21] ANSARI T，CHANDRA G，GUPTA P K，et al. Synthesis of pine needle cyanoethyl cellulose using Taguchi L25 orthogonal array[J]. Industrial Crops & Products，2023，191：115973.
[22] 田志强，姜兴宇，杨国哲，等.一种面向航天复杂构件的柔性作业车间能耗优化调度问题研究[J]. 机械工程学报，2023，59(8)：273-287. TIAN Zhiqiang，JIANG Xingyu，YANG Guozhe，et al. Energy efficient scheduling of flexible job shop with aerospace complex components[J]. Journal of Mechanical Engineering，2023，59(8)：273-287.
[23] LI Y，YANG Z，WANG L，et al. A hybrid imperialist competitive algorithm for energy-efficient flexible job shop scheduling problem with variable-size sublots[J]. Computers & Industrial Engineering，2022，172： 108641.
[24] ZHENG X L，WANG L. A collaborative multiobjective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem[J]. IEEE Transactions on Systems，Man，and Cybernetics：Systems，2016，48(5)：790-800.

考虑预保养的批量流柔性作业车间绿色调度

Lot Streaming Flexible Job-shop Scheduling with Pre-maintenance

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	胡炳涛, 钟锐锐, 冯毅雄, 杨晨, 王天跃, 洪兆溪, 谭建荣. 人-信息-物理互联环境下数字车间制造能力建模与自适应调度[J]. 机械工程学报, 2025, 61(3): 23-39.
[2]	方遒, 宋豪杰, 卢弘, 毛建旭, 王耀南. 集成工人和AGV的多要素柔性作业车间调度方法[J]. 机械工程学报, 2025, 61(18): 330-343.
[3]	安友军, 刘成, 陈晓慧, 高开周, 董元发, 孟荣华. 具有设备动态性和加工速度选择的生产与维护集成调度研究[J]. 机械工程学报, 2025, 61(14): 362-382.
[4]	梁志珍, 王晓佳. 双资源约束的柔性作业车间鲁棒调度方法[J]. 机械工程学报, 2024, 60(6): 114-126.
[5]	刘琼, 刘嘉豪, 刘佳良. 基于改进人工蜂群算法的预防性维修与柔性作业车间成组调度集成优化[J]. 机械工程学报, 2023, 59(12): 89-96.
[6]	吕岩, 徐正军, 李聪波, 李玲玲, 杨秒. 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.
[7]	孟磊磊, 张彪, 任亚平, 张超勇. 求解分布式柔性作业车间调度的混合蛙跳算法[J]. 机械工程学报, 2021, 57(17): 263-272.
[8]	李聪波, 王睿, 寇阳, 吕岩, 赵希坤. 考虑设备预维护的柔性作业车间调度节能优化方法[J]. 机械工程学报, 2021, 57(10): 220-230.
[9]	石小秋, 李炎炎, 邓丁山, 龙伟. 基于自适应变级遗传杂草算法的FJSP研究[J]. 机械工程学报, 2019, 55(6): 223-232.
[10]	李明, 雷德明. 考虑准备时间和关键目标的柔性作业车间低碳调度研究[J]. 机械工程学报, 2019, 55(21): 139-149.
[11]	魏鑫, 张泽群, 唐敦兵, 杨长祺, 金永乔, 秦威. 面向节能的导弹结构件混线生产作业车间多目标调度研究[J]. 机械工程学报, 2018, 54(9): 45-54.
[12]	李聪波, 沈欢, 李玲玲, 易茜. 面向能耗的多工艺路线柔性作业车间分批优化调度模型^*[J]. 机械工程学报, 2017, 53(5): 12-23.
[13]	徐华, 张庭. 混合离散蝙蝠算法求解多目标柔性作业车间调度^*[J]. 机械工程学报, 2016, 52(18): 201-212.
[14]	赵诗奎. 求解柔性作业车间调度问题的两级邻域搜索混合算法[J]. 机械工程学报, 2015, 51(14): 175-184.
[15]	彭建刚;刘明周;张铭鑫;张玺;葛茂根. 基于改进非支配排序的云模型进化多目标柔性作业车间调度[J]. , 2014, 50(12): 198-205.