Multi-objective flow-shop scheduling optimization based on positive projection grey target model

被引：0
作者：
Zhu G. [1 ,2 ]
Zhang Z. [1 ]
机构：
[1] School of Advanced Manufacturing, Fuzhou University, Quanzhou
[2] School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 04期
关键词：
CRITIC method; Entropy weight method; Genetic algorithms; Multi-objective optimization; Permutation flow-shop scheduling; Positive projection grey target; Projective target distance;
D O I：
10.13196/j.cims.2022.04.012
中图分类号：
学科分类号：
摘要：
To obtain high-quality solutions and good performance solution sets during the optimization of many-objective Permutation Flow-shop Scheduling Problems(PFSP), a positive projection grey target model with comprehensive objective weight was proposed based on grey target theory, which could overcome the shortcoming of obtaining information in the optimization process. A PFSP mathematical model with four-objective and a grey target model in the field of multi-objective optimization were defined, then target distance was calculated to judge the pros and cons of Pareto front and to extract the uncertainty information among the objective function values. To solve the different target distances of Pareto front in same section plane and to get more information in the solution space, a Positive Projection Grey Target(PPGT)model was proposed. Further, to acquire the information of the volatility and the correlation among the objective function values, a novel comprehensive objective weight method based on CRITIC method and entropy weight method was introduced into PPGT model. The modified PPGT model was integrated into the genetic algorithm to solve the multi-objective PFSP. The effectiveness of the proposed method was verified by three sets of experiments and four comparison algorithms. © 2022, Editorial Department of CIMS. All right reserved.
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收藏
页码：1087 / 1098
页数：11
相关论文
共 30 条
  • [1] LI Yang, LI Xinyu, MOU Jianhui, Large-scale permutation flow-shop scheduling method based on improved simulated annealing algorithm, Computer Integrated Manufacturing Systems, 26, 2, pp. 366-375, (2020)
  • [2] ZHANG W B, ZHU G Y., A multi-objective optimization of PCB prototyping assembly with OFA based on similarity of intuitionistic fuzzy sets[J], IEEE Transactions on Fuzzy Systems, 29, 7, pp. 2054-2061, (2021)
  • [3] GHEISARIHA E, TAVANA M, JOLAI F, Et al., A simulation-optimization model for solving flexible flow shop scheduling problems with rework and transportation, Mathematics and Computers in Simulation, 180, pp. 152-178, (2021)
  • [4] ZHU G Y, DING C, ZHANG W B., Optimal foraging algorithm that incorporates fuzzy relative entropy for solving many objective permutation flow shop scheduling problems[J], IEEE Transactions on Fuzzy Systems, 28, 11, pp. 2738-2746, (2020)
  • [5] LIANG J, WANG P, GUO L, Et al., Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution[J], Memetic Computing, 11, 6, pp. 407-422, (2019)
  • [6] ANJANA V, SRIDHARAN R, KUMAR P., Metaheuristics for solving a multi-objective flow shop scheduling problem with sequence-dependent setup times[J], Journal of Scheduling, 23, 2, pp. 49-69, (2020)
  • [7] LIU Jianchang, LI Fei, WANG Honghai, Et al., Survey on evolutionary many-objective optimization algorithms, Control and Decision, 33, 5, pp. 879-887, (2018)
  • [8] ZHU G Y, HE L J, JU X W, Et al., A fitness assignment strategy based on the grey and entropy parallel analysis and its application to MOEA[J], European Journal of Operational Research, 265, 3, pp. 813-828, (2018)
  • [9] HE L J, LI W F, ZHANG Y, Et al., A discrete multi-objective fireworks algorithm for flow-shop scheduling with sequence-dependent setup times, Swarm and Evolutionary Computation, 51, (2019)
  • [10] ZHANG Zhuang, LI Linlin, WEI Zhenhua, Et al., Dynamic effectiveness evaluation of command and control system based on variable weight-projection grey target, Systems Engineering and Electronics, 41, 4, pp. 801-809, (2019)
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