Journal of System Simulation ›› 2024, Vol. 36 ›› Issue (1): 131-148.doi: 10.16182/j.issn1004731x.joss.22-1038
• Papers • Previous Articles
Qin Hongbin1,2(), Li Chenxiao1(
), Tang Hongtao1, Zhang Feng1
Received:
2022-09-05
Revised:
2022-11-17
Online:
2024-01-20
Published:
2024-01-19
Contact:
Li Chenxiao
E-mail:qhbwhut@163.com;licx_7@163.com
CLC Number:
Qin Hongbin, Li Chenxiao, Tang Hongtao, Zhang Feng. Reentrant Hybrid Flow Shop Scheduling Problem Based on MOMA[J]. Journal of System Simulation, 2024, 36(1): 131-148.
Table 1
Notation used in this study
符号 | 含义 | 符号 | 含义 |
---|---|---|---|
工件总数 | |||
工件索引, | 第 | ||
工件 | 在批处理机上加工的总批数 | ||
工件 | 在批处理机上加工的批数索引, | ||
工位数 | 批处理机上第 | ||
工位索引, | 批处理机上第 | ||
工位 | 机器运转的额定运行功率 | ||
工位 | 机器空转的额定运行功率 | ||
工件 | 机器调整的额定运行功率 | ||
工件 | 工件 | ||
工件 | 工件 | ||
工序 | 在工位l上加工的所有工序的集合 | ||
一个充分大的正数 | |||
决策变量, | |||
决策变量, | |||
第 | 决策变量,工件i被分配到任务批 | ||
决策变量,任务批 | |||
工件 | 决策变量,任务批 |
Table 4
Results in theof orthogonal experiment
实验编号 | 参数 | IGD平均值 | |||
---|---|---|---|---|---|
Pop | g | d | fl | ||
1 | 1(20) | 1(0.6) | 1(4) | 1(0.5) | 42.151 8 |
2 | 1(20) | 2(0.7) | 2(5) | 2(1.0) | 44.914 6 |
3 | 1(20) | 3(0.8) | 3(6) | 3(1.5) | 56.702 3 |
4 | 2(30) | 1(0.6) | 2(5) | 3(1.5) | 43.343 2 |
5 | 2(30) | 2(0.7) | 3(6) | 1(0.5) | 42.746 3 |
6 | 2(30) | 3(0.8) | 1(4) | 2(1.0) | 32.771 5 |
7 | 3(40) | 1(0.6) | 3(6) | 2(1.0) | 36.112 3 |
8 | 3(40) | 2(0.7) | 1(4) | 3(1.5) | 55.272 4 |
9 | 3(40) | 3(0.8) | 2(5) | 1(0.5) | 42.666 6 |
Table 6
Comparison results with MA
算例 | 规模 工件,工序,重入 | GD | IGD | SP | |||
---|---|---|---|---|---|---|---|
MOMA | MA | MOMA | MA | MOMA | MA | ||
j10p4r1 | (10,4,1) | 1.619 1 | 2.315 3 | 9.672 8 | 11.485 3 | 2.542 6 | 3.124 5 |
j11p4r1 | (11,4,1) | 1.897 0 | 3.453 6 | 15.308 3 | 20.753 4 | 6.071 5 | 7.286 4 |
j12p4r1 | (12,4,1) | 2.949 1 | 2.554 5 | 29.105 9 | 30.417 5 | 7.330 7 | 8.954 2 |
j14p5r1 | (14,5,1) | 6.120 3 | 8.586 3 | 30.159 3 | 34.247 8 | 7.554 7 | 10.354 2 |
j15p5r1 | (15,5,1) | 6.005 8 | 7.356 4 | 54.512 5 | 49.451 2 | 19.535 5 | 26.168 5 |
j20p5r1 | (20,5,1) | 4.635 2 | 3.214 8 | 26.530 6 | 28.147 5 | 9.832 7 | 10.783 5 |
j14p6r1 | (14,6,1) | 3.231 9 | 5.369 7 | 28.259 2 | 29.478 7 | 9.052 3 | 10.553 6 |
j17p6r1 | (17,6,1) | 3.620 2 | 4.534 7 | 33.992 7 | 36.147 5 | 9.100 1 | 11.896 5 |
j20p6r1 | (20,6,1) | 2.610 8 | 3.457 4 | 28.610 3 | 30.583 1 | 7.636 3 | 8.314 5 |
j29p4r1 | (29,4,1) | 4.525 0 | 5.572 1 | 38.457 2 | 42.364 8 | 10.829 4 | 11.235 1 |
j30p4r3 | (30,4,3) | 8.760 8 | 12.714 2 | 128.599 7 | 178.315 2 | 16.583 5 | 19.325 8 |
j45p4r5 | (45,4,5) | 9.444 8 | 14.142 3 | 161.301 7 | 204.432 9 | 26.774 7 | 35.742 8 |
j26p5r4 | (26,5,4) | 8.401 9 | 15.484 7 | 110.351 5 | 163.418 3 | 22.590 6 | 28.168 4 |
j37p5r2 | (37,5,2) | 4.602 8 | 5.142 3 | 58.011 3 | 76.415 8 | 16.638 1 | 18.328 5 |
j28p5r1 | (28,5,1) | 3.346 2 | 4.586 6 | 41.165 2 | 40.238 4 | 14.540 8 | 18.452 3 |
j21p6r1 | (21,6,2) | 4.765 4 | 5.143 7 | 47.255 5 | 55.123 7 | 13.864 3 | 15.153 2 |
j28p6r3 | (28,6,3) | 29.208 3 | 34.639 7 | 153.480 6 | 236.813 5 | 26.321 2 | 35.628 1 |
j39p6r1 | (39,6,1) | 4.161 9 | 5.786 2 | 37.115 4 | 45.364 1 | 12.178 3 | 17.152 4 |
Table 7
Comparison results of four algorithmic metrics for small -scale examples
算例 | 规模 工件,工序,重入 | 指标 | MOMA | NSGA-II | MOPSO | MOGWO |
---|---|---|---|---|---|---|
j10p4r1 | (10,4,1) | GD | 1.619 1 | 3.366 8 | 5.264 3 | 6.121 0 |
IGD | 9.672 8 | 18.385 2 | 13.199 3 | 23.143 8 | ||
SP | 2.542 6 | 8.396 7 | 12.537 3 | 15.215 2 | ||
j11p4r1 | (11,4,1) | GD | 1.897 0 | 2.349 1 | 3.006 0 | 4.404 0 |
IGD | 15.308 3 | 22.517 2 | 20.349 1 | 28.960 6 | ||
SP | 6.071 5 | 7.839 8 | 11.094 5 | 12.209 2 | ||
j12p4r1 | (12,4,1) | GD | 2.949 1 | 3.784 2 | 5.621 7 | 7.111 8 |
IGD | 29.105 9 | 28.118 9 | 41.681 1 | 56.993 2 | ||
SP | 7.330 7 | 16.288 1 | 22.052 1 | 27.329 7 | ||
j14p5r1 | (14,5,1) | GD | 6.120 3 | 6.272 7 | 11.388 4 | 13.508 5 |
IGD | 30.159 3 | 39.528 4 | 45.344 5 | 60.104 6 | ||
SP | 7.554 7 | 15.636 3 | 20.669 5 | 19.849 0 | ||
j15p5r1 | (15,5,1) | GD | 6.005 8 | 6.270 0 | 12.013 7 | 14.246 4 |
IGD | 54.512 5 | 50.734 5 | 89.750 0 | 110.996 2 | ||
SP | 19.535 5 | 39.350 8 | 42.221 3 | 44.380 0 | ||
j20p5r1 | (20,5,1) | GD | 4.635 2 | 5.835 3 | 9.922 3 | 13.000 2 |
IGD | 26.530 6 | 33.572 6 | 46.876 3 | 69.919 6 | ||
SP | 9.832 7 | 18.536 7 | 24.883 8 | 22.066 6 | ||
j14p6r1 | (14,6,1) | GD | 3.231 9 | 2.973 1 | 4.255 8 | 5.477 8 |
IGD | 28.259 2 | 26.888 8 | 30.958 1 | 42.854 4 | ||
SP | 9.052 3 | 14.762 6 | 17.979 8 | 20.670 9 | ||
j17p6r1 | (17,6,1) | GD | 3.620 2 | 4.506 1 | 8.426 3 | 9.425 0 |
IGD | 33.992 7 | 41.298 8 | 55.982 1 | 66.524 4 | ||
SP | 9.100 1 | 15.430 6 | 29.191 2 | 22.456 5 | ||
j20p6r1 | (20,6,1) | GD | 2.610 8 | 3.277 2 | 5.860 8 | 6.070 7 |
IGD | 28.610 3 | 28.993 8 | 44.372 4 | 56.933 7 | ||
SP | 7.636 3 | 19.884 6 | 29.011 0 | 30.051 3 |
Table 8
Comparison results of four algorithmic metrics for large -scale examples
算例 | 规模 工件,工序,重入 | 指标 | MOMA | NSGA-II | MOPSO | MOGWO |
---|---|---|---|---|---|---|
j29p4r1 | (29,4,1) | GD | 4.525 0 | 4.639 9 | 9.678 7 | 10.411 8 |
IGD | 38.457 2 | 40.196 1 | 57.567 1 | 77.635 6 | ||
SP | 10.829 4 | 18.819 9 | 26.741 1 | 29.875 2 | ||
j30p4r3 | (30,4,3) | GD | 8.760 8 | 12.491 6 | 21.377 5 | 23.974 6 |
IGD | 128.599 7 | 224.099 6 | 240.870 2 | 356.212 6 | ||
SP | 16.583 5 | 31.455 5 | 54.017 8 | 51.516 9 | ||
j45p4r5 | (45,4,5) | GD | 9.444 8 | 12.620 8 | 18.250 6 | 20.846 2 |
IGD | 161.301 7 | 227.415 4 | 291.005 9 | 362.785 2 | ||
SP | 26.774 7 | 53.005 3 | 64.142 3 | 79.052 9 | ||
j26p5r4 | (26,5,4) | GD | 8.401 9 | 11.908 5 | 15.733 3 | 22.195 1 |
IGD | 110.351 5 | 188.709 5 | 145.532 6 | 249.983 1 | ||
SP | 22.590 6 | 35.522 5 | 64.906 9 | 76.323 9 | ||
j37p5r2 | (37,5,2) | GD | 4.602 8 | 6.421 5 | 10.250 5 | 9.768 5 |
IGD | 58.011 3 | 60.513 7 | 87.023 9 | 107.917 9 | ||
SP | 16.638 1 | 36.356 1 | 41.764 2 | 50.787 5 | ||
j28p5r1 | (28,5,1) | GD | 3.346 2 | 3.938 5 | 5.177 4 | 6.665 8 |
IGD | 41.165 2 | 45.774 3 | 49.973 2 | 90.048 5 | ||
SP | 14.540 8 | 20.264 4 | 23.771 3 | 27.632 5 | ||
j21p6r1 | (21,6,2) | GD | 4.765 4 | 7.302 0 | 14.208 1 | 17.247 0 |
IGD | 47.255 5 | 62.638 0 | 73.649 6 | 109.067 4 | ||
SP | 13.864 3 | 28.908 5 | 43.442 6 | 51.014 2 | ||
j28p6r3 | (28,6,3) | GD | 29.208 3 | 49.331 4 | 99.923 3 | 99.264 2 |
IGD | 153.480 6 | 371.098 8 | 332.256 4 | 463.690 3 | ||
SP | 26.321 2 | 64.158 8 | 101.393 8 | 93.153 3 | ||
j39p6r1 | (39,6,1) | GD | 4.161 9 | 4.538 7 | 7.634 0 | 9.169 7 |
IGD | 37.115 4 | 36.080 4 | 58.864 9 | 76.029 7 | ||
SP | 12.178 3 | 21.209 3 | 21.274 8 | 25.168 1 |
1 | Lin Chuncheng, Liu Wanyu, Chen Y H. Considering Stockers in Reentrant Hybrid Flow Shop Scheduling with Limited Buffer Capacity[J]. Computers & Industrial Engineering, 2020, 139: 106154. |
2 | Graves S C, Meal H C, Stefek D, et al. Scheduling of Re-entrant Flow Shops[J]. Journal of Operations Management, 1983, 3(4): 197-207. |
3 | Kumar R, Tiwari M K, Allada V. Modelling and Rescheduling of a Re-entrant Wafer Fabrication Line Involving Machine Unreliability[J]. International Journal of Production Research, 2004, 42(21): 4431-4455. |
4 | 顾涛, 李苏建, 林莹璐, 等. 周期式退火炉作批处理机的可重入批离散机流水车间调度[J]. 机械工程学报, 2020, 56(2): 220-232. |
Gu Tao, Li Sujian, Lin Yinglu, et al. Research on the Re-entrant Batch Discrete Flow Shop Scheduling for Periodic Annealing Furnace as Batch Processor[J]. Journal of Mechanical Engineering, 2020, 56(2): 220-232. | |
5 | 李颖俐, 李新宇, 高亮. 混合流水车间调度问题研究综述[J]. 中国机械工程, 2020, 31(23): 2798-2813, 2828. |
Li Yingli, Li Xinyu, Gao Liang. Review on Hybrid Flow Shop Scheduling Problems[J]. China Mechanical Engineering, 2020, 31(23): 2798-2813, 2828. | |
6 | Yan Xiaoyan, Wu Xiuli. An Improved Reentrant-bottleneck Heuristic for the Reentrant Hybrid Flow Shop Scheduling[C]//2020 Chinese Automation Congress (CAC). Piscataway, NJ, USA: IEEE, 2020: 4170-4175. |
7 | Yan Xiaoyan, Wu Xiuli. IMOEA/D to Optimize Job Release Problem for a Reentrant Hybrid Flow Shop[J]. Computers & Industrial Engineering, 2022, 163: 107800. |
8 | Hekmatfar M, Fatemi Ghomi S M T, Karimi B. Two Stage Reentrant Hybrid Flow Shop with Setup Times and the Criterion of Minimizing Makespan[J]. Applied Soft Computing, 2011, 11(8): 4530-4539. |
9 | Achmad Pratama Rifai, Setyo Tri Windras Mara, Sudiarso Andi. Multi-objective Distributed Reentrant Permutation Flow Shop Scheduling with Sequence-dependent Setup Time[J]. Expert Systems with Applications, 2021, 183: 115339. |
10 | Chamnanlor C, Sethanan K, Chien C F, et al. Hybrid Genetic Algorithms for Solving Reentrant Flow-shop Scheduling with Time Windows[J]. Industrial Engineering and Management Systems, 2013, 12(4): 306-316. |
11 | Chamnanlor Chettha, Sethanan Kanchana, Gen Mitsuo, et al. Embedding Ant System in Genetic Algorithm for Re-entrant Hybrid Flow Shop Scheduling Problems with Time Window Constraints[J]. Journal of Intelligent Manufacturing, 2017, 28(8): 1915-1931. |
12 | Mason S J, Oey K. Scheduling Complex Job Shops Using Disjunctive Graphs: A Cycle Elimination Procedure[J]. International Journal of Production Research, 2003, 41(5): 981-994. |
13 | Jia Wenyou, Jiang Zhibin, Li You. Combined Scheduling Algorithm for Re-entrant Batch-processing Machines in Semiconductor Wafer Manufacturing[J]. International Journal of Production Research, 2015, 53(6): 1866-1879. |
14 | Jia Wenyou, Chen Hao, Liu Li, et al. Full-batch-oriented Scheduling Algorithm on Batch Processing Workstation of β1→β2 Type with Re-entrant Flow[J]. International Journal of Computer Integrated Manufacturing, 2017, 30(10): 1029-1042. |
15 | Jing Xuelei, Pan Quanke, Gao Liang, et al. An Effective Iterated Greedy Algorithm for the Distributed Permutation Flowshop Scheduling with Due Windows[J]. Applied Soft Computing, 2020, 96: 106629. |
16 | Cho Hang-Min, Bae Suk-Joo, Kim Jungwuk, et al. Bi-objective Scheduling for Reentrant Hybrid Flow Shop Using Pareto Genetic Algorithm[J]. Computers & Industrial Engineering, 2011, 61(3): 529-541. |
17 | Zhou Binghai, Hu Liman, Zhong Zhenyi. A Hybrid Differential Evolution Algorithm with Estimation of Distribution Algorithm for Reentrant Hybrid Flow Shop Scheduling Problem[J]. Neural Computing and Applications, 2018, 30(1): 193-209. |
18 | 董君, 叶春明, 万孟然. 考虑可再生能源的可重入混合流水车间调度问题[J]. 计算机集成制造系统, 2022, 28(4): 1112-1128. |
Dong Jun, Ye Chunming, Wan Mengran. Reentrant Hybrid Flow Shop Scheduling Problem with Renewable Energy[J]. Computer Integrated Manufacturing Systems, 2022, 28(4): 1112-1128. | |
19 | Zervoudakis Konstantinos, Tsafarakis Stelios. A Mayfly Optimization Algorithm[J]. Computers & Industrial Engineering, 2020, 145: 106559. |
20 | Guo Xiaokai, Yan Xianguo, Jermsittiparsert Kittisak. Using the Modified Mayfly Algorithm for Optimizing the Component Size and Operation Strategy of a High Temperature PEMFC-powered CCHP[J]. Energy Reports, 2021, 7: 1234-1245. |
21 | A M Shaheen Mohamed, Hasanien Hany M, El Moursi M S, et al. Precise Modeling of PEM Fuel Cell Using Improved Chaotic MayFly Optimization Algorithm[J]. International Journal of Energy Research, 2021, 45(13): 18754-18769. |
22 | Majumdar Kingsuk, Kumar Roy Provas, Banerjee Subrata. Implementation of Multi-objective Chaotic Mayfly Optimisation for Hydro-thermal-solar-wind Scheduling Based on Available Transfer Capability Problem[J]. International Transactions on Electrical Energy Systems, 2021, 31(11): e13029. |
23 | Liu Zhenkun, Jiang Ping, Wang Jianzhou, et al. Ensemble Forecasting System for Short-term Wind Speed Forecasting Based on Optimal Sub-model Selection and Multi-objective Version of Mayfly Optimization Algorithm[J]. Expert Systems with Applications, 2021, 177: 114974. |
24 | Gao Zhengming, Li Suruo, Zhao Juan, et al. Self-organizing Hierarchical Mayfly Optimization Algorithm[C]//2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). Piscataway, NJ, USA: IEEE, 2020: 355-358. |
25 | Bhattacharyya Trinav, Chatterjee Bitanu, Pawan Kumar Singh, et al. Mayfly in Harmony: A New Hybrid Meta-heuristic Feature Selection Algorithm[J]. IEEE Access, 2020, 8: 195929-195945. |
26 | Rim Cholmin, Songhao Piao, Li Guo, et al. A Niching Chaos Optimization Algorithm for Multimodal Optimization[J]. Soft Computing, 2018, 22(2): 621-633. |
27 | 刘公致, 吴琼, 王光义, 等. 改进型Logistic混沌映射及其在图像加密与隐藏中的应用[J]. 电子与信息学报, 2022, 44(10): 3602-3609. |
Liu Gongzhi, Wu Qiong, Wang Guangyi, et al. A Improved Logistic Chaotic Map and Its Application to Image Encryption and Hiding[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3602-3609. | |
28 | Wang Yongzhen, Chen Yan, Lin Yan. Memetic Algorithm Based on Sequential Variable Neighborhood Descent for the Minmax Multiple Traveling Salesman Problem[J]. Computers & Industrial Engineering, 2017, 106: 105-122. |
29 | Han Honggui, Zhang Linlin, Hou Ying, et al. Adaptive Candidate Estimation-assisted Multi-objective Particle Swarm Optimization[J]. Science China Technological Sciences, 2022, 65(8): 1685-1699. |
30 | 潘楚光, 谭平, 熊瑞平, 等. 基于MOGWO的拟人机械手连杆长度优化[J]. 组合机床与自动化加工技术, 2022(6): 19-23. |
Pan Chuguang, Tan Ping, Xiong Ruiping, et al. Optimization of Link Lengths of Anthropomorphic Manipulator Based on MOGWO[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2022(6): 19-23. | |
31 | 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 Liping, Ren Yu, Qiu Qicang, et al. Survey on Performance Indicators for Multi-objective Evolutionary Algorithms[J]. Chinese Journal of Computers, 2021, 44(8): 1590-1619. |
[1] | Li Wenjing, Luo Yanlin, Wang Yuhui, Zhu Li. Virtual Navigation Path Planning Based on Octree Potential Field for Endonasal Endoscope [J]. Journal of System Simulation, 2023, 35(9): 2054-2063. |
[2] | Xu Wang, Weidong Ji, Guohui Zhou. Fault Indicator Configuration Optimization Based on Cooperative Game Particle Swarm Algorithm [J]. Journal of System Simulation, 2023, 35(6): 1278-1289. |
[3] | Jiaying Yu, Hongli Zhang, Yingchao Dong. Research on No-Wait Flow Shop Scheduling Based on Discrete State Transition Algorithm [J]. Journal of System Simulation, 2023, 35(5): 1034-1045. |
[4] | Yue Ma, Lin Wu, Shengming Guo. Research on Modeling and Solution Method of Operational Tasks Assignment [J]. Journal of System Simulation, 2023, 35(4): 887-898. |
[5] | Yue Ma, Lin Wu, Yun Liu, Guangzhao Ding. Research on Modeling and Solution Method of Operational Tasks Optimization [J]. Journal of System Simulation, 2023, 35(3): 470-483. |
[6] | Xu Wang, Weidong Ji, Guohui Zhou, Jiahui Yang. Multi-objective Optimization Algorithm Based on Multi-index Elite Individual Game Mechanism [J]. Journal of System Simulation, 2023, 35(3): 494-514. |
[7] | Guangqiu Huang, Xixuan Zhao, Qiuqin Lu. Calculation of Optimal VOCs Emission Reduction Based on Improved SEIRS Model in Cloud Environment [J]. Journal of System Simulation, 2023, 35(3): 632-645. |
[8] | Damin Zhang, Yi Wang, Linna Zhang. Chameleon Swarm Algorithm for Segmental Variation Learning of Population and S-type Weight [J]. Journal of System Simulation, 2023, 35(1): 11-26. |
[9] | Xuewen Wu, Jingxian Liao. Game-Based Resource Allocation and Task Offloading Scheme in Collaborative Cloud-Edge Computing System [J]. Journal of System Simulation, 2022, 34(7): 1468-1481. |
[10] | Xiangyang Deng, Limin Zhang, Wei Fang, Miao Tang. Robot Path Planning Based on Bidirectional Aggregation Ant Colony Optimization [J]. Journal of System Simulation, 2022, 34(5): 1101-1108. |
[11] | Jianfeng Li, Di Lu, Hexiang Li. An Improved Atomic Search Algorithm [J]. Journal of System Simulation, 2022, 34(3): 490-502. |
[12] | Jun Dong, Chunming Ye. Research on Joint Optimization of Energy-Saving Distributed Manufacturing and Preventive Maintenance for Semiconductor Wafers [J]. Journal of System Simulation, 2022, 34(3): 584-602. |
[13] | Wang Fuyu, Tang Tao. Application of Two-population Fish Swarm Algorithm in Distributed Portfolio [J]. Journal of System Simulation, 2021, 33(9): 2074-2084. |
[14] | Liu Jingsen, Yuan Mengmeng, Li Yu. Solving Engineering Optimization Design Problems Based on Improved Salp Swarm Algorithm [J]. Journal of System Simulation, 2021, 33(4): 854-866. |
[15] | Gu Juping, Cheng Tianyu, Wang Jianping, Hua Liang, Zhao Fengshen, Jiang Ling. Fast 3D Medical Image Registration Based on Geometric Feature Invariants [J]. Journal of System Simulation, 2020, 32(11): 2105-2111. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||