[1] 李伯虎,张霖,王时龙,等. 云制造——面向服务的网络化制造新模式[J]. 计算机集成制造系统, 2010, 16(1):1-7,16. [2] 李伯虎, 张霖, 任磊,等.再论云制造[J].计算机集成制造系统, 2011,17(3):449-457. [3] LI F, ZHANG L, LIAO T W, et al. Multi-objective optimisation of multi-task scheduling in cloud manufacturing[J]. International Journal of Production Research, 2019, 57(12): 3847-3863. [4] LIU Y, XU X, ZHANG L, et al. Workload-based multi-task scheduling in cloud manufacturing[J]. Robotics and Computer-integrated manufacturing, 2017, 45: 3-20. [5] 何巍,贾国柱,刘春婷,等.基于客户心理期望的云制造多任务调度优化[J].系统工程,2018,36(7):39-46. [6] HU Y, ZHU F, ZHANG L, et al. Scheduling of manufac-turers based on chaos optimization algorithm in cloud manufacturing[J]. Robotics and Computer-Integrated Manufacturing, 2019, 58: 13-20. [7] 万雨松,余开朝.云制造生产模式下的生产调度研究[J].软件导刊,2022,21(10):142-148. [8] 李伯虎, 张霖, 柴旭东. 云制造概论[J].中兴通讯技术, 2010 (4): 5-8. [9] ZHOU L, ZHANG L, ZHAO C, et al. Diverse task scheduling for individualized requirements in cloud manufacturing[J]. Enterprise Information Systems, 2018, 12(3): 300-318. [10] 胡艳娟, 朱非凡, 王艺霖, 等.云制造环境下的资源调度研究综述[J]. 制造技术与机床, 2018(3): 33-39. [11] LIU Y, WANG L, WANG X V, et al. Scheduling in cloud manufacturing: state-of-the-art and research challenges[J]. International Journal of Production Research, 2019, 57(15-16): 4854-4879. [12] CHENG Y, TAO F, ZHAO D, et al. Modeling of manufacturing service supply-demand matching hypernetwork in service-oriented manufacturing systems[J]. Robotics and Computer-Integrated Manufacturing, 2017, 45: 59-72. [13] 周龙飞,张霖,刘永奎.云制造调度问题研究综述[J].计算机集成制造系统,2017,23(6):1147-1166. [14] REN L, CUI J, WEI Y, et al. Research on the impact of service provider cooperative relationship on cloud manufacturing platform[J]. The International Journal of Advanced Manufacturing Technology, 2016, 86: 2279-2290. [15] 李学国,沈应兰.基于云制造服务平台的船舶协同合作技术研究[J].舰船科学技术,2016,38(18):97-99. [16] LI W, ZHU C, YANG L T, et al. Subtask scheduling for distributed robots in cloud manufacturing[J]. IEEE Systems Journal, 2015, 11(2): 941-950. [17] 王旭亮,柴旭东,张程,等.云制造环境下跨企业协同生产调度算法[J].计算机集成制造系统,2019,25(2):412-420. [18] CHEN J, MO R, CHU J, et al. Research on the optimal combination and scheduling method of crowdsourcing members in a cloud design platform[J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2019, 233(11): 2196-2209. [19] XU B, TANG Y, ZHU Y, et al. Bilateral Collaborative Optimization for Cloud Manufacturing Service[J]. Computers, Materials & Continua, 2020, 64(3):2031-2042. [20] 王天日,张敏敏,刘娟,等.考虑云制造服务协同的多用户任务调度优化[J].计算机集成制造系统,2023,29(9):3006-3017. [21] 舒萧,王时龙,康玲,等.面向云制造的有限资源多任务调度博弈[J].重庆大学学报,2020,43(3):1-11. [22] LIU S, ZHANG L, ZHANG W, et al. Game theory based multi-task scheduling of decentralized 3D printing services in cloud manufacturing[J]. Neurocomputing, 2021, 446: 74-85. [23] LIU Z H, WANG Z J, YANG C. Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing[J]. Advances in Manufacturing, 2019, 7: 374-388. [24] LIU S, LI L, ZHANG L, et al. Game theory based dynamic event-driven service scheduling in cloud manufacturing[J]. IEEE Transactions on Automation Science and Engineering, 2022,21(1):618-629. [25] 王雪萍,谢灿,高新勤,等.基于博弈论的云制造服务平台价格战应对策略研究[J].工业工程,2020,23(1):53-58. [26] 张坤鹏,王艳,纪志成.基于不完全信息博弈的云制造群智能优化方法[J].系统仿真学报,2024,36(4):915-928. [27] 马南峰,姚锡凡,陈飞翔,等.面向工业5.0的人本智造[J].机械工程学报,2022,58(18):88-102. [28] 罗浩嘉,潘大志.改进布谷鸟算法求解双资源约束柔性车间调度问题[J].计算机应用研究,2022,39(8):2295-2300. [29] WANG D, QIAO F, GUAN L, et al. Human-machine collaborative decision-making method based on confidence for smart workshop dynamic scheduling[J]. IEEE Robotics and Automation Letters, 2022, 7(3): 7850-7857. [30] TAN W, YUAN X, WANG J, et al. A fatigue-conscious dual resource constrained flexible job shop scheduling problem by enhanced NSGA-Ⅱ: An application from casting workshop[J]. Computers & Industrial Engineering, 2021, 160: 107557. [31] GUO Q. Task scheduling based on ant colony optimization in cloud environment[C]//2017 5th International Conference on Computer-Aided Design,Manufacturing,Modeling and Simulation (CDMMS).[S.l.]:[s.n.], 2017. [32] 李雪,李芳.云环境下大规模定制中资源配置研究[J].工业工程,2021,24(1):147-154. [33] 冯晨微,王艳.云制造系统并行任务优化调度[J].系统仿真学报,2019,31(12):2626-2635. [34] YUAN M, CAI X, ZHOU Z, et al. Dynamic service resources scheduling method in cloud manufacturing environment[J]. International Journal of Production Research, 2021, 59(2): 542-559. [35] 吴秀丽,孙阳君.机器多转速的柔性作业车间绿色调度问题[J].计算机集成制造系统,2018,24(4):862-875. [36] MANSOURI S A, AKTAS E, BESIKCI U. Green schedu-ling of a two-machine flowshop: Trade-off between makes-pan and energy consumption[J]. European Journal of Operational Research, 2016, 248(3): 772-788. [37] JIANG T, DENG G. Optimizing the low-carbon flexible job shop scheduling problem considering energy consumption[J]. IEEE Access, 2018, 6: 46346-46355. [38] 杨立熙,王秀萍.考虑低碳的柔性作业车间调度问题研究[J].组合机床与自动化加工技术,2018(6):168-171,176. [39] YANG D, LIU Q, LI J, et al. Multi-objective optimization of service selection and scheduling in cloud manufacturing considering environmental sustainability[J]. Sustainability, 2020, 12(18): 7733. [40] GU J, JIANG T, ZHU H, et al. Low-carbon job shop scheduling problem with discrete genetic-grey wolf optimization algorithm[J]. Journal of Advanced Manufacturing Systems, 2020, 19(1): 1-14. [41] ZHOU L, WANG Y, LIU P, et al. A framework for energy-saving selection and scheduling of equipment resources in a networked manufacturing mode[J]. The International Journal of Advanced Manufacturing Technology, 2023, 128(3/4): 1845-1862. [42] 彭高贤,文一凭,刘建勋,等.能耗感知的云制造服务选择与调度优化方法[J].计算机集成制造系统,2024,30(8):2697-2707. [43] 何巍,贾国柱,孔继利,等.基于可持续性的云制造多任务调度[J].中国机械工程,2018,29(18):2215-2225. [44] 侯天天,张守京.基于改进离散蜉蝣算法的双资源柔性车间可持续调度方法[J].机电工程,2023,40(3):408-414. [45] LUO S, ZHANG L, FAN Y. Improved nondominated sorting genetic algorithm-Ⅱ for bi-objective flexible job-shop scheduling problem[C]//2020 IEEE Symposium Series on Computational Intelligence (SSCI).[S.l.]:IEEE, 2020: 2616-2624. [46] 贺智明,刘敏.云环境下基于偏好的资源公平分配策略[J].计算机工程与科学,2017,39(11):1991-1999. [47] LIN T L, HORONG S J, KAO T W, et al. An efficient job-shop scheduling algorithm based on particle swarm optimization[J]. Expert Systems with Applications, 2010, 37(3): 2629-2636. [48] GONG G, DENG Q, CHIONG R, et al. An effective memetic algorithm for multi-objective job-shop scheduling[J]. Knowledge-Based Systems, 2019, 182: 104840. [49] LIU M, YAO X, LI Y. Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems[J]. Applied Soft Computing, 2020, 87: 105954. [50] SHI S, XIONG H. Solving the multi-objective job shop scheduling problems with overtime consideration by an enhanced NSGA-Ⅱ[J]. Computers & Industrial Engineering, 2024: 110001. [51] ENGIN O, CERAN G, YILMAZ M K. An efficient genetic algorithm for hybrid flow shop scheduling with multipro-cessor task problems[J]. Applied Soft Computing, 2011, 11(3): 3056-3065. [52] SHAO W, PI D, SHAO Z. Optimization of makespan for the distributed no-wait flow shop scheduling problem with iterated greedy algorithms[J]. Knowledge-Based Systems, 2017, 137: 163-181. [53] GONG D, HAN Y, SUN J. A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems[J]. Knowledge-Based Systems, 2018, 148: 115-130. [54] ZHAO Z Y, ZHOU M C, LIU S X. Iterated greedy algorithms for flow-shop scheduling problems: A tutorial[J]. IEEE Transactions on Automation Science and Engineering, 2021, 19(3): 1941-1959. [55] ZHANG G, SHAO X, LI P, et al. An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem[J]. Computers & Industrial Engineering, 2009, 56(4): 1309-1318. [56] WANG L, ZHOU G, XU Y, et al. An effective artificial bee colony algorithm for the flexible job-shop scheduling problem[J]. The International Journal of Advanced Manufacturing Technology, 2012, 60: 303-315. [57] LI M, LEI D. An imperialist competitive algorithm with feedback for energy-efficient flexible job shop scheduling with transportation and sequence-dependent setup times[J]. Engineering Applications of Artificial Intelligence, 2021, 103: 104307. [58] SONG W, CHEN X, LI Q, et al. Flexible job-shop scheduling via graph neural network and deep reinforcement learning[J]. IEEE Transactions on Industrial Informatics, 2022, 19(2): 1600-1610. [59] 刘娟,陈华平.基于云模型的PSO算法求解差异工件单机批调度问题[J].计算机系统应用,2010,19(2):164-168. [60] 牟健慧,潘全科,牟建彩,等.基于遗传变邻域混合算法的带交货期的单机车间逆调度方法[J].机械工程学报,2018,54(3):148-159. [61] 王金凤,陈璐,杨雯慧.考虑设备可用性约束的单机调度问题[J].上海交通大学学报,2021,55(1):103-110. [62] 宋存利,竺啸天.求解复杂混合流水车间调度的改进NSGAII算法[J].计算机仿真,2024,41(3):379-387. [63] 郝晨晨,李芳.智慧云环境下基于改进蚁群算法的资源协同并行调度研究[J].物流工程与管理,2017,39(3):44-47. [64] LI K, ZHANG H J, CHENG B Y, et al. Uniform parallel machine scheduling problems with fixed machine cost[J]. Optimization Letters, 2018, 12: 73-86. [65] ZHANG H, LI K, CHU C, et al. Parallel batch processing machines scheduling in cloud manufacturing for minimizing total service completion time[J]. Computers & Operations Research, 2022, 146: 105899. [66] 张洁,张朋,刘国宝.基于两阶段蚁群算法的带非等效并行机的作业车间调度[J].机械工程学报,2013,49(6):136-144. [67] 鲁建厦,胡庆辉,董巧英,等.面向云制造的混流混合车间调度问题[J].中国机械工程,2017,28(2):191-198,205. [68] 王贞,张纪会,齐元青.具有空闲时间的云制造作业车间调度方法[J].控制与决策,2017,32(5):811-816. [69] 董海,戴瑶,张天瑞.云制造模式下基于变邻域动态烟花算法的柔性车间调度[J].组合机床与自动化加工技术,2019(7): 130-133. [70] 李云龙,罗国富,文笑雨,等.基于混合遗传算法的云制造环境下柔性作业车间调度方案[J].轻工学报,2020,35(3):99-108. [71] JIANG P, DING J L, GUO Y. Application and dynamic simulation of improved genetic algorithm in production workshop scheduling[J]. International Journal of Simulation Modelling, 2018, 17(1): 159-169. [72] YUAN M, LI Y, ZHANG L, et al. Research on intelligent workshop resource scheduling method based on improved NSGA-Ⅱ algorithm[J]. Robotics and Computer-Integrated Manufacturing, 2021, 71: 102141. [73] 吕盛坪,乔立红.工艺规划与车间调度及两者集成的研究现状和发展趋势[J].计算机集成制造系统,2014,20(2):290-300. [74] 安玉伟,严洪森.柔性作业车间生产计划与调度集成优化求解策略[J].自动化学报,2013,39(9):1476-1491. [75] LI X, GAO L, PAN Q, et al. An effective hybrid genetic algorithm and variable neighborhood search for integrated process planning and scheduling in a packaging machine workshop[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 49(10): 1933-1945. [76] LIAN K, ZHANG C, GAO L, et al. Integrated process planning and scheduling using an imperialist competitive algorithm[J]. International Journal of Production Research, 2012, 50(15): 4326-4343. [77] LIU Q, LI X, GAO L, et al. A modified genetic algorithm with new encoding and decoding methods for integrated process planning and scheduling problem[J]. IEEE Transactions on Cybernetics, 2020, 51(9): 4429-4438. [78] AKBARIPOUR H, HOUSHMAND M, VAN WOENSEL T, et al. Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models[J]. The International Journal of Advanced Manufacturing Technology, 2018, 95: 43-70. [79] ZHOU L, ZHANG L, FANG Y. Logistics service scheduling with manufacturing provider selection in cloud manufacturing[J]. Robotics and Computer-Integrated Manufacturing, 2020, 65: 101914. [80] LI X, WANG X, ZHAO Y, et al. Improved grey wolf optimization algorithm for solving cloud manufacturing scheduling problem with limit logistics resource[C]//2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD).[S.l.]:IEEE, 2021: 174-178. [81] LIU S, DENG Q, LIU X, et al. Dual-service integrated scheduling of manufacturing and logistics for multiple tasks in cloud manufacturing[J]. Expert Systems with Applications, 2024, 235: 121129. [82] LI Y, GU W, YUAN M, et al. Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network[J]. Robotics and Computer-Integrated Manufacturing, 2022, 74: 102283. [83] 朱传军,冯诗健,张超勇,等.考虑设备预防性维护的开放车间调度问题[J].中国机械工程,2023,34(14):1693-1700. [84] 李佳磊,顾幸生.双种群混合遗传算法求解具有预防性维护的分布式柔性作业车间调度问题[J].控制与决策,2023, 38(2):475-482. [85] WANG H, SHENG B, LU Q, et al. A novel multi-objective optimization algorithm for the integrated scheduling of flexible job shops considering preventive maintenance activities and transportation processes[J]. Soft Computing, 2021, 25: 2863-2889. [86] AN Y, CHEN X, GAO K, et al. Multiobjective flexible job-shop rescheduling with new job insertion and machine preventive maintenance[J]. IEEE Transactions on Cybernetics, 2022,53(5):3101-3113. [87] 晏鹏宇,杨柳,车阿大.共享制造平台供需匹配与调度研究综述[J].系统工程理论与实践,2022,42(3):811-832. [88] 路世昌,关弼元.基于云制造环境下的航天云网云制造平台物流调度优化问题研究[J].制造业自动化,2020,42(10):67-71,94. [89] WANG X, ZHANG L, LIU Y, et al. Dynamic scheduling of tasks in cloud manufacturing with multi-agent reinforcement learning[J]. Journal of Manufacturing Systems, 2022, 65: 130-145. [90] 杨建,牟丽莎,刘述木,等.云制造服务平台在舰船协同制造中的应用[J].舰船科学技术,2018,40(10):187-189. [91] 段静波,潘惠苹.云制造环境下船舶制造服务平台的构建[J].舰船科学技术,2016,38(22):157-159. [92] 刘刚,何建佳.云制造环境下分布式3D打印任务调度研究[J].机械设计与制造,2023(6):125-129. [93] ZHANG L, LUO X, REN L, et al. Cloud based 3D printing service platform for personalized manufacturing[J]. Science China. Information Sciences, 2020,63(2):124201. |