Hybrid Genetic Algorithm for Distributed Flow Shop Inverse Scheduling Problem

被引：0
|
作者：
Mu J. [1 ]
Duan P. [2 ]
Gao L. [3 ]
Peng W. [4 ]
Cong J. [5 ]
机构：
[1] School of Mechatronics and Automotive Engineering, Yantai University, Yantai
[2] School of Computer and Control Engineering, Yantai University, Yantai
[3] School of Mechanical Engineering and Science, Huazhong University of Science and Technology, Wuhan
[4] School of Economics and Management, Yantai Universty, Yantai
[5] School of Mechanical Engineering, Shandong University of Technology, Zibo
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2022年 / 58卷 / 06期
关键词：
Distributed scheduling; Flow shop scheduling; Hybrid genetic algorithm; Inverse scheduling; Population coordination;
D O I：
10.3901/JME.2022.06.295
中图分类号：
学科分类号：
摘要：
Distributed scheduling is a new mode of intelligent manufacturing, which is in urgent need of new scheduling methods to meet the Dynamic and changeable market demand. To solve the distributed permutation flow shop problem, the inverse scheduling method is used to optimize the job shop scheduling by minimizing the processing parameters. Aiming at minimizing the adjusted processing time, a mathematical model of flow shop reverse scheduling is established, and a hybrid genetic optimization Algorithm is proposed under the framework of genetic algorithm. Firstly, based on the characteristics of the inverse scheduling parameters, an improved operation-based decimal mechanism double coding scheme is proposed, which can adjust the parameters and ensure the possible solution. Secondly, a hybrid initialization method is adopted by improving the NEH heuristic method and the rule-based method, in order to coordinate the ability of global search and local search, the local search strategy and the double-population cooperative search strategy with learning mechanism are designed. In order to verify the performance of the proposed algorithm, three algorithms are compared and analyzed based on the problem examples. The results show that the proposed algorithm can solve the distributed pipeline inverse scheduling problem more effectively. © 2022 Journal of Mechanical Engineering.
引用
收藏
页码：295 / 308
页数：13
相关论文
共 33 条
  • [1] WU Xiuli, LIU Xiajing, Differential evolution algorithm for solving distributed flexible job shop scheduling problem, Computer Integrated Manufacturing Systems, 25, 10, pp. 2539-2558, (2019)
  • [2] QIAN Bin, WANG Ling, HUANG Dexian, Et al., Rolling strategy and optimization algorithm for dynamic no-wait flow shop scheduling problem, Control and Decision, 24, 4, pp. 481-487, (2009)
  • [3] SAUER J, SUELMANN G, APPELRATH H J., Multi-site scheduling with fuzzy concepts, International Journal of Approximate Reasoning, 19, 1, pp. 145-160, (1998)
  • [4] ZHANG Hao, ZHAO Fu, SUTHERLAND J W., Energy-efficient scheduling of multiple manufacturing factories under real-time electricity pricing, CIRP Annals-Manufacturing Technology, 64, 1, pp. 41-44, (2015)
  • [5] RIBAS I, COMPANYS R, TORT-MARTORELL X., Efficient heuristics for the parallel blocking flow shop scheduling problem, Expert Systems with Applications, 74, pp. 41-54, (2017)
  • [6] BEHNAMIAN J., Matheuristic for the decentralized factories scheduling problem, Applied Mathematical Modelling, 47, pp. 668-684, (2017)
  • [7] NADERI B, RUIZ R., A scatter search algorithm for the distributed permutation flowshop scheduling problem, European Journal of Operational Research, 239, 2, pp. 323-334, (2014)
  • [8] FRAMINAN J M., A bounded search iterated greedy algorithm for the distributed permutation flowshop scheduling problem, International Journal of Production Research, 53, 4, pp. 1111-1123, (2015)
  • [9] GAO J, CHEN R, DENG W, Et al., An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem, International Journal of Production Research, 51, 3, pp. 641-651, (2013)
  • [10] BEHNAMIAN J, GHOMI S M T F., Minimizing cost-related objective in synchronous scheduling of parallel factories in the virtual production network, Applied Soft Computing, 29, pp. 221-232, (2015)
1234