[1] 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.
[2] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[3] 张伟, 曾思通. 基于NSGA-II的多目标流水车间调度问题研究[J]. 机电技术, 2017(6): 46-50.
ZHANG W, ZENG S T. Research on multi-objective flow shop scheduling problem based on NSGA-II[J]. Electro-Mechanical Technology, 2017(6): 46-50.
[4] TAN W H, 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 work-shop[J]. Computers & Industrial Engineering, 2021, 160: 107557.
[5] 王莹莹, 张贞凯. 基于改进 NSGA-II 的雷达通信一体化信号功率优化设计[J]. 火力与指挥控制, 2022,47(1): 150-155.
WANG Y Y, ZHANG Z K. Optimal design of signal power for integrated radar and communication based on improved NSGA-II[J]. Fire Control & Command Control, 2022, 47(1): 150-155.
[6] 陆科苗, 何利力. 基于改进NSGA-Ⅱ混合算法求解多目标柔性作业车间调度问题[J]. 智能计算机与应 用, 2022, 12(7): 46-51.
LU K M, HE L L. Solving multi-objective flexible job shop scheduling problem based on improved NSGA-Ⅱ hybrid algorithm[J]. Intelligent Computer and Application, 2022, 12(7): 46-51.
[7] CHEN J, NING T, XU G, et al. A memetic algorithm for energy-efficient scheduling of integrated production and shipping[J]. International Journal of Computer Integrated Manufacturing, 2022, 35(10/11): 1246-1268.
[8] LI G, LI X, GAO L, et al. Tasks assigning and sequencing of multiple AGVs based on an improved harmony search algorithm[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(11): 4533-4546.
[9] 陈魁, 毕利. 考虑运输时间的多目标柔性作业车间调度研究[J]. 小型微型计算机系统, 2021, 42(5): 946-952.
CHEN K, BI L. Research on multi-objective flexible job shop scheduling considering transport time[J]. Journal of Chinese Computer Systems, 2021, 42(5): 946-952.
[10] HIDRI L, ELKOSANTINI S, MABKHOT M. Exact and heuristic procedures for the two-center hybrid flow shop scheduling problem with transportation times[J]. IEEE Access, 2018, 6: 21788-21801.
[11] NISHI T, HIRANAKA Y, GROSSMANN I E. A bilevel decomposition algorithm for simultaneous production scheduling and conflict-free routing for automated guided vehicles[J]. Computers & Operations Research, 2011, 38(5): 876-888.
[12] 赵宁, 李开典, 田青, 等. 考虑运输时间柔性作业车间调度问题的快速寻优方法[J]. 计算机集成制造系统, 2015, 21(3): 724-732.
ZHAO N, LI K D, TIAN Q, et al. Fast optimization approach of flexible job shop scheduling with transport time consideration[J]. Computer Integrated Manufacturing Systems, 2015, 21(3): 724-732.
[13] ZABIHZADEH S S, REZAEIAN J. Two meta-heuristic algorithms for flexible flow shop scheduling problem with robotic transportation and release time[J]. Applied Soft Computing, 2016, 40: 319-330.
[14] MA P C, TAO F, LIU Y L, et al. A hybrid particle swarm optimization and simulated annealing algorithm for job-shop scheduling[C]//Proceedings of the 2014 IEEE International Conference on Automation Science and Engineering, 2014: 125-130.
[15] SEENU N, KUPPAN C R M, RAMYA M M, et al. Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems[J]. Industrial Robot, 2020, 47(6): 929-942.
[16] 黄平, 孟永钢. 最优化理论与方法[M]. 北京:清华大学出版社, 2009: 189-201.
HUANG P, MENG Y G. Optimization theory and method[M]. Beijing: Tsinghua University Press, 2009: 189-201.
[17] 高亮, 张国辉, 王晓娟. 柔性作业车间调度智能算法及 其应用[M]. 武汉: 华中科技大学出版社, 2012:102-107.
GAO L, ZHANG G H, WANG X J. Flexible job shop scheduling intelligent algorithm and its application[M]. Wuhan: Huazhong University of Science and Technology Press, 2012: 102-107.
[18] 全燕鸣, 何一明. 多机器人任务分配调度的克隆选择算法[J]. 华南理工大学学报 (自然科学版), 2021, 49(5): 102-110.
QUAN Y M, HE Y M. Research on clonal selection algorithm for multi-robot task allocation and scheduling[J]. Journal of South China University of Technology (Natural Science Edition), 2021, 49(5): 102-110.
[19] BAGHISHANI H, MOHAMMADZADEH M. A data cloning algorithm for computing maximum likelihood estimates in spatial generalized linear mixed models[J]. Computational Statistics & Data Analysis, 2011, 55(4): 1748-1759.
[20] 王丽萍, 任宇, 邱启仓, 等. 多目标进化算法性能评价 指标研究综述[J]. 计算机学报, 2021, 44(8): 1590-1619.
WANG L P, REN Y, QIU Q C, et al. Survey on performance indicators for multi-objective evolutionary algorithms[J]. Chinese Journal of Computers, 2021, 44(8): 1590-1619.
[21] COELLO C A C, CORTéS N C. Solving multi-objective optimization problems using an artificial immune system[J]. Genetic Programming and Evolvable Machines, 2005, 6(2): 163-190.
[22] LI Z P, LI X T. Research on model and algorithm of task allocation and path planning for multi-robot[J]. Open Journal of Applied Sciences, 2017, 7(10): 511-519. |