Group distributed manufacturing process resource allocation based on chaos genetic algorithm

被引：0
|
作者：
Li Y.-B. [1 ,2 ]
Song D.-L. [1 ,2 ]
Wang L. [1 ,2 ]
机构：
[1] School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan
[2] Hubei Digital Manufacturing Key Laboratory, Wuhan University of Technology, Wuhan
来源：
Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 06期
关键词：
Chaos-improved algorithm; Genetic algorithm; Group distributed manufacturing; Multi-objective optimization; Pareto optimal solution; Process-level resources allocation;
D O I：
10.13195/j.kzyjc.2017.1526
中图分类号：
学科分类号：
摘要：
There are some problems in group distributed manufacturing enterprises, such as the scatter of geographically location, the mismatching of manufacturing resources and abilities, the coexistence of resources idle and shortage etc. Based on the characteristics of multi-agent, multi-task, multi-resource, multi-process and co-ordination in the process of manufacturing resource allocation, the optimal model of distributed manufacturing resource allocation is proposed to balance the overall interests of the group and the individual interests of the sub-ordinate enterprises. An improved genetic algorithm based on Logistic chaos is designed to obtain the Pareto optimal solution of the model. Finally, an example with its analysis is given to demonstrate the effectiveness of the proposed model and algorithm. © 2019, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：1178 / 1186
页数：8
相关论文
共 13 条
  • [1] Wang L., Guo S., Li X., Distributed manufacturing resource selection strategy in cloud manufacturing, Int J of Advanced Manufacturing Technology, 94, 9, pp. 3375-3388, (2018)
  • [2] Liu H.W., Yuan D.P., Zheng F., Optimization of production allocation and algorithm design based on group distributed manufacturing, Operations Research and Management Science, 26, 1, pp. 1-7, (2017)
  • [3] Du B.G., Guo S.S., Peng Z., Multi-agent manufacturing resource allocation of outsourcing order in group manufacturing, Computer Integrated Manufacturing Systems, 21, 2, pp. 455-466, (2015)
  • [4] Zhou K., Lu M., Wang G., Collaborative optimization of manufacture task decomposition and resource deployment of manufacturing unit, J of Harbin Institute of technology, 41, 11, pp. 47-52, (2009)
  • [5] Wang S.L., Song W.Y., Kang L., Manufacturing resource allocation based on cloud manufacturing, Computer Integrated Manufacturing Systems, 18, 7, pp. 1396-1405, (2012)
  • [6] Guo G., Shen L., Yang L.X., Resource matching mode of collaborative manufacturing process for outsourcing planning of equipment products, J of Chongqing University, 35, 5, pp. 29-34, (2012)
  • [7] Wang J., Sun S., Multiform hybrid decision for TOC product mix optimization with extending capacity of outsourcing based on immune algorithm, Acta Aeronautica Et Astronautica Sinica, 28, 5, pp. 1216-1229, (2007)
  • [8] Wang L., Deng J., Wang S.Y., Survey on optimization algorithms for distributed shop scheduling, Control and Decision, 31, 1, pp. 1-11, (2016)
  • [9] Liu D.B., Chen Y.J., Zhang Z.Q., Virtual enterprise dynamic supervision mechanism based on the third supervisory organization, Computer Integrated Manufacturing Systems, 15, 10, pp. 2073-2079, (2009)
  • [10] Liao G.C., Tsao T.P., Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting, IEEE Trans on Evolutionary Computation, 10, 3, pp. 330-340, (2006)
12