[1] 郑锦灿,邵立珍,雷雪梅.一种基于改进NSGA-Ⅱ的多目标绿色柔性作业车间调度方法[J].制造技术与机床, 2023(1):145-152. ZHENG J C, SHAO L Z, LEI X M. A multi-objective green flexible job shop scheduling method based on improved NSGA-Ⅱ algorithm[J]. Manufacturing Technology&Machine Tool, 2023(1):145-152.(in Chinese) [2] ZHANG G H, LU X X, LIU X, et al. An effective two-stage algorithm based on convolutional neural network for the bi-objective flexible job shop scheduling problem with machine breakdown[J]. Expert Systems with Applications, 2022, 203:117460. [3] GONG G L, CHIONG R, DENG Q W, et al. A two-stage memetic algorithm for energy-efficient flexible job shop scheduling by means of decreasing the total number of machine restarts[J]. Swarm and Evolutionary Computation, 2022, 75:101131. [4] FAN J X, SHEN W M, GAO L, et al. A hybrid Jaya algorithm for solving flexible job shop scheduling problem considering multiple critical paths[J]. Journal of Manufacturing Systems, 2021, 60:298-311. [5] CHEN R H, YANG B, LI S, et al. A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem[J]. Computers&Industrial Engineering, 2020, 149:106778. [6] SHI X Q, LONG W, LI Y Y, et al. Different performances of different intelligent algorithms for solving FJSP:a perspective of structure[J]. Computational Intelligence and Neuroscience, 2018, 2018:4617816. [7] 王粟,陈新彦,曾亮.基于混合GA算法求解车间调度问题[J].计算机工程与设计, 2022, 43(5):1304-1311. WANG S, CHEN X Y, ZENG L. Solving job shop scheduling problem based on hybrid GA algorithm[J]. Computer Engineering and Design, 2022, 43(5):1304-1311.(in Chinese) [8] 李瑞,龚文引.改进的基于分解的多目标进化算法求解双目标模糊柔性作业车间调度问题[J].控制理论与应用, 2022, 39(1):31-40. LI R, GONG W Y. An improved multi-objective evolutionary algorithm based on decomposition for bi-objective fuzzy flexible job-shop scheduling problem[J]. Control Theory&Applications, 2022, 39(1):31-40.(in Chinese) [9] GAO D, WANG G G, PEDRYCZ W. Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism[J]. IEEE Transactions on Fuzzy Systems, 2020, 28(12):3265-3275. [10] LI J Q, LIU Z M, LI C D, et al. Improved artificial immune system algorithm for type-2 fuzzy flexible job shop scheduling problem[J]. IEEE Transactions on Fuzzy Systems, 2021, 29(11):3234-3248. [11] VELA C R, AFSAR S, PALACIOS J J, et al. Evolutionary tabu search for flexible due-date satisfaction in fuzzy job shop scheduling[J]. Computers&Operations Research, 2020, 119:104931. [12] 李尚函,胡蓉,钱斌,等.超启发式遗传算法求解模糊柔性作业车间调度[J].控制理论与应用, 2020, 37(2):316-330. LI S H, HU R, QIAN B, et al. Hyper-heuristic genetic algorithm for solving fuzzy flexible job shop scheduling problem[J]. Control Theory&Applications, 2020, 37(2):316-330.(in Chinese) [13] LIN J, ZHU L, WANG Z J. A hybrid multi-verse optimization for the fuzzy flexible job-shop scheduling problem[J]. Computers&Industrial Engineering, 2019, 127:1089-1100. [14] LI R, GONG W Y, LU C. A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling[J]. Expert Systems with Applications, 2022, 203:117380. [15] 王春,田娜,纪志成,等.求解模糊柔性作业车间调度的多目标进化算法[J].电子学报, 2017, 45(12):2909-2916. WANG C, TIAN N, JI Z C, et al. Multi-objective evolutionary algorithm to solve fuzzy flexible job shop scheduling problem[J]. Acta Electronica Sinica, 2017, 45(12):2909-2916.(in Chinese) [16] SAKAWA M, MORI T. An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate[J]. Computers and Industrial Engineering, 1999, 36(2):325-341. [17] LI R, GONG W Y, LU C. Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time[J]. Computers&Industrial Engineering, 2022, 168:108099. [18] ZHANG Q F, LI H. MOEA/D:a multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6):712-731. [19] ZHANG G H, GAO L, SHI Y. An effective genetic algorithm for the flexible job-shop scheduling problem[J]. Expert Systems with Applications, 2011, 38(4):3563-3573. [20] WANG J J, WANG L. Decoding methods for the flow shop scheduling with peak power consumption constraints[J]. International Journal of Production Research, 2019, 57(10):3200-3218. [21] LUO Q, DENG Q W, GONG G L, et al. A distributed flexible job shop scheduling problem considering worker arrangement using an improved memetic algorithm[J]. Expert Systems with Applications, 2022, 207:117984. [22] LEI D M, LI M, WANG L. A two-phase meta-heuristic for multiobjective flexible job shop scheduling problem with total energy consumption threshold[J]. IEEE Transactions on Cybernetics, 2019, 49(3):1097-1109. [23] DÍAZ-GALIÁN M V, VEGA-RODRÍGUEZ M A. Many-objective approach based on problem-aware mutation operators for protein encoding[J]. Information Sciences:an International Journal, 2022, 613(C):376-400. [24] LUO S, ZHANG L X, FAN Y S. Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization[J]. Journal of Cleaner Production, 2019, 234:1365-1384. [25] 徐文豪,王艳,严大虎,等.花授粉算法求解多目标模糊柔性作业车间调度[J].系统仿真学报, 2018, 30(11):4403-4412. XU W H, WANG Y, YAN D H, et al. Flower pollination algorithm for multi-objective fuzzy flexible job shop scheduling[J]. Journal of System Simulation, 2018, 30(11):4403-4412.(in Chinese) [26] AN Y J, CHEN X H, HU J W, et al. Joint optimization of preventive maintenance and production rescheduling with new machine insertion and processing speed selection[J]. Reliability Engineering&System Safety, 2022, 220:108269. [27] ZHENG W, SUN J Y. Two-stage hybrid learning-based multi-objective evolutionary algorithm based on objective space decomposition[J]. Information Sciences:an International Journal, 2022, 610:1163-1186. |