复杂制造系统人机交互调度与案例研究

doi:10.3901/JME.2025.15.351

摘要/Abstract

摘要： 制造系统正经历以客户为中心的柔性制造模式的深刻转变，逐步向多阶段集成化和分布式网络化扩展，存在复杂多维目标集成决策的现实化需求。传统的基于算法或机器智能的一体化调度方法在处理非线性约束及难以量化的复杂业务底线诉求时存在局限，且决策者难以预知和干预容易产生沉没成本。因此，提出复杂制造系统中的人机交互调度技术，旨在通过人机交互机制，实现决策者对优化方向的灵活调控，结合业务诉求与决策偏好对搜索解空间进行预优化并分割以提升求解效率，实现人机协同下的调度优化。针对具有集约生产与多样交付决策诉求的家电制造系统，设计了两级交互式调度方法，通过约束边界调整的方式平衡业务矛盾与体现决策者的偏好诉求，并构建了约束规划模型及不同目标导向的邻域结构。最后，利用某家电企业的历史案例数据进行验证，结果显示调度方案置信度高且获用户认可，证明了方法的有效性。

关键词: 人本智造, 复杂制造系统, 人机交互调度, 人机协同优化, 柔性约束

Abstract: The manufacturing system is undergoing a profound transformation towards a customer-centric flexible manufacturing model, progressively evolving into multi-stage integration and distributed networking. This evolution necessitates integrated decision-making to address complex multi-dimensional objectives. Traditional scheduling methods, which rely on algorithms or machine intelligence, face limitations when it comes to addressing nonlinear constraints and accurately quantifying intricate business-related lower-bound requirements. Additionally, decision-makers often face challenges in anticipating and intervening to mitigate potential sunk costs. To address these challenges, a human-machine interactive scheduling technology for complex manufacturing systems is proposed. The proposed approach aims to enable flexible control over optimization through human-machine interaction mechanisms. By combining business requirements with decision-maker preferences, the solution space is pre-optimized and partitioned to improve efficiency, achieving scheduling and optimization through human-machine collaboration. For an appliance manufacturing system with intensive production and diverse delivery requirements, a two-stage interactive scheduling method is proposed. This approach balances business conflicts and reflects decision-maker preferences through constraint boundary adjustments. A constraint programming model is developed, along with neighborhood structures tailored to different objective orientations. Finally, the proposed method is validated using historical case data from a home appliance enterprise. The results demonstrate that the proposed scheme exhibits high reliability and gains user acceptance, confirming its effectiveness.

Key words: human-centric smart manufacturing, complex manufacturing system, human-machine interactive scheduling, human-machine collaborative optimization, flexible constraints

中图分类号:

TP18

刘建军, 邱俊豪, 张忠明. 复杂制造系统人机交互调度与案例研究[J]. 机械工程学报, 2025, 61(15): 351-371.

LIU Jianjun, QIU Junhao, ZHANG Zhongming. Human-computer Interaction Scheduling and Case Studies in Complex Manufacturing System[J]. Journal of Mechanical Engineering, 2025, 61(15): 351-371.

参考文献

[1] FU Yaping, HOU Yushuang, WANG Zifan, et al. Distributed scheduling problems in intelligent manufacturing systems[J]. Tsinghua Science and Technology, 2021, 26(5):625-645.
[2] 丁进良,杨翠娥,陈远东,等.复杂工业过程智能优化决策系统的现状与展望[J].自动化学报, 2018, 44(11):1931-1943. DING Jinliang, YANG Cuie, CHEN Yuandong, et al. Research progress and prospects of intelligent optimization decision making in complex industrial process[J]. Acta Automatica Sinica, 2018, 44(11):1931-1943.
[3] FONTES D B M M, HOMAYOUNI S M. Joint production and transportation scheduling in flexible manufacturing systems[J]. Journal of Global Optimization, 2019, 74(4):879-908.
[4] SAWIK T. Integrated supply, production and distribution scheduling under disruption risks[J]. Omega, 2016, 62:131-144.
[5] LU Shan, XIE Lei, ZHU Li, et al. Integrated scheduling of a hybrid manufacturing and recovering system in a multi-product multi-stage environment with carbon emission[J]. Journal of Cleaner Production, 2019, 222:695-709.
[6] GUO Zhaoxia, ZHANG Dongqing, LEUNG S Y S, et al. A bi-level evolutionary optimization approach for integrated production and transportation scheduling[J]. Applied Soft Computing, 2016, 42:215-228.
[7] ZHANG Zhengpei, FU Yaping, GAO Kaizhou, et al. A cooperative evolutionary algorithm with simulated annealing for integrated scheduling of distributed flexible job shops and distribution[J]. Swarm and Evolutionary Computation, 2024, 85:101467.
[8] REN Minglun, CHEN Nengying, QIU Hui. Human-machine collaborative decision-making:An evolutionary roadmap based on cognitive intelligence[J]. International Journal of Social Robotics, 2023, 15(7):1101-1114.
[9] 李祥文,宋程,丁帅.人机协同决策中的人因能力评估研究[J].中国管理科学, 2024, 32(3):145-155. LI Xiangwen, SONG Cheng, DING Shuai. Research on human factors capability assessment in human-machine collaborative decision-making[J]. Chinese Journal of Management Science, 2024, 32(3):145-155.
[10] INOUE T, FURUHASHI T, MAEDA H, et al. A proposal of combined method of evolutionary algorithm and heuristics for nurse scheduling support system[J]. IEEE Transactions on Industrial Electronics, 2003, 50(5):833-838.
[11] HE Yingdong, HE Zhen, KIM K, et al. A robust interactive desirability function approach for multiple response optimization considering model uncertainty[J]. IEEE Transactions on Reliability, 2020, 70(1):175-187.
[12] NGUYEN S, ZHANG M, ALAHAKOON D, et al. People-centric evolutionary system for dynamic production scheduling[J]. IEEE Transactions on Cybernetics, 2019, 51(3):1403-1416.
[13] KALISZEWSKI I. Using trade-off information in decision-making algorithms[J]. Computers&Operations Research, 2000, 27(2):161-182.
[14] HE Yunfei, SUN Chenyuan, MENG Li, et al. Flexible drug-target interaction prediction with interactive information extraction and trade-off[J]. Expert Systems with Applications, 2024, 249:123821.
[15] CHANG Yinghua, WU Tzting. Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems[J]. Applied Mathematics and Computation, 2011, 218(8):4075-4089.
[16] CHOU Y L, LIN T Y, WU J Z, et al. An interactive method for multi-criteria dispatching problems with unknown preference functions[J]. Computers&Industrial Engineering, 2020, 144:106462.
[17] ALMEIDA A T, ALMEIDA J A, COSTA A P C S, et al. A new method for elicitation of criteria weights in additive models:Flexible and interactive tradeoff[J]. european Journal of Operational Research, 2016, 250(1):179-191.
[18] QIN Yingjie, ZHENG Jiehui, WU Qinghua. Many-objective interactive optimization and decision making for distribution network expansion planning[J]. Control Engineering Practice, 2021, 116:104917.
[19] 姚锡凡,马南峰,张存吉,等.以人为本的智能制造:演进与展望[J].机械工程学报, 2022, 58(18):2-15. YAO Xifan, MA Nanfeng, ZHANG Cunji, et al. Human-centric smart manufacturing:Evolution and outlook[J]. Journal of Mechanical Engineering, 2022, 58(18):2-15.
[20] KESSLER M, ARLINGHAUS J C. A framework for human-centered production planning and control in smart manufacturing[J]. Journal of Manufacturing Systems, 2022, 65:220-232.
[21] LU Chao, GAO Liang, YI Jie, et al. Energy-efficient scheduling of distributed flow shop with heterogeneous factories:A real-world case from automobile industry in China[J]. IEEE Transactions on Industrial Informatics, 2020, 17(10):6687-6696.
[22] ABDELJAOUED M A, SAADANI N E H, BAHROUN Z. Heuristic and metaheuristic approaches for parallel machine scheduling under resource constraints[J]. Operational Research, 2020, 20:2109-2132.
[23] QIU Junhao, LIU Jianjun, PENG Chengfeng, et al. A novel predictive-reactive scheduling method for parallel batch processor lot-sizing and scheduling with sequence-dependent setup time[J]. Computers&Industrial Engineering, 2024, 189:109985.
[24] HU Zhenyang, HU Guiping. A multi-stage stochastic programming for lot-sizing and scheduling under demand uncertainty[J]. Computers&Industrial Engineering, 2018, 119:157-166.
[25] 刘建军,李钦颂,曾创锋,等.柔性装配流水车间调度与分批配送集成问题研究[J/OL].计算机集成制造系统, 1-25[2024-12-28]. https://doi.org/10.13196/j.cims. 2023.0502. LIU Jianjun, LI Qinsong, ZENG Chuangfeng, et al. Flexible assembly flowshop scheduling with batch delivery[J]. Computer Integrated Manufacturing System, 1-25[2024-12-28]. https://doi.org/10.13196/j.cims. 2023.0502.
[26] PANG Shibao, GUO Shunsheng, WANG Lei, et al. Mass personalization-oriented integrated optimization of production task splitting and scheduling in a multi-stage flexible assembly shop[J]. Computers&Industrial Engineering, 2021, 162:107736.
[27] 张家谔,杨建军.面向复杂作业车间的交互式两级调度方法[J].控制与决策, 2020, 35(9):2285-2291. ZHANG Jiae, YANG Jianjun. Two-stage interactive scheduling method for complex job-shop[J]. Control and Decision, 2020, 35(9):2285-2291.
[28] WANG Baicun, ZHENG Pai, YIN Yue, et al. Toward human-centric smart manufacturing:A human-cyberphysical systems (HCPS) perspective[J]. Journal of Manufacturing Systems, 2022, 63:471-490.
[29] PERGHER I, FREJ E A, ROSELLI L R P, et al. Integrating simulation and FITradeoff method for scheduling rules selection in job-shop production systems[J]. International Journal of Production Economics, 2020, 227:107669.
[30] NING Chao, YOU Fengqi. Optimization under uncertainty in the era of big data and deep learning:When machine learning meets mathematical programming[J]. Computers&Chemical Engineering, 2019, 125:434-448.
[31] JIN Yaochu, WANG Handing, CHUGH T, et al. Data-driven evolutionary optimization:An overview and case studies[J]. IEEE Transactions on Evolutionary Computation, 2018, 23(3):442-458.
[32] QIU Junhao, LIU Jianjun, LI Zhantao, et al. A multi-level action coupling reinforcement learning approach for online two-stage flexible assembly flow shop scheduling[J]. Journal of Manufacturing Systems, 2024, 76:351-370.
[33] LUO Wenjian, YI Ruikang, YANG Bin, et al. Surrogate-assisted evolutionary framework for data-driven dynamic optimization[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 3(2):137-150.
[34] 赵诗奎.作业车间调度问题的多工序联动邻域结构研究[J].机械工程学报, 2020, 56(13):192-206. ZHAO Shikui. Research on multi-operation joint movement neighborhood structure of job shop scheduling problem[J]. Journal of Mechanical Engineering, 2020, 56(13):192-206.
[35] HE Xuan, PAN Quanke, GAO Liang, et al. A greedy cooperative co-evolutionary algorithm with problem-specific knowledge for multiobjective flowshop group scheduling problems[J]. IEEE Transactions on Evolutionary Computation, 2021, 27(3):430-444.