Research on resources optimisation deployment model and algorithm for collaborative manufacturing process

被引:6
作者
Changfeng, Y. [1 ]
Dinghua, Z. [1 ]
Wenli, P. [1 ]
Kun, B. [1 ]
机构
[1] Northwestern Polytech Univ, Minist Educ, Key Lab Cont Design & Integrated Mfg Technol, Xian 710072, Peoples R China
关键词
logical manufacturing unit; logical manufacturing process; physical manufacturing unit; executive manufacturing process; resources optimising deployment; genetic algorithm;
D O I
10.1080/00207540500478520
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Agility is the competitive advantage in the global manufacturing environment. It is believed that agility can be realised by networked manufacturing resource optimisation deployment. However, this is a challenge to us now. To solve this question, logical manufacturing unit and logical manufacturing process are proposed to decompose and model the networked manufacturing task, and networked manufacturing resources are organised and modelled based on physical manufacturing unit. During the deployment of manufacturing resources to the task, many factors should be taken into consideration. Of these, manufacturing cost, time and quality are the most important factors. In this paper, before these factors are considered, networked manufacturing resources pre-deployment is carried out to find the candidate manufacturing resources on the basis of manufacturing abilities. Then, resources optimisation deployment is modelled as a multi-objectives optimisation. This optimisation problem is solved based on genetic algorithm after transforming the multi-objectives optimisation problem to a single objectives optimisation problem. Although we may not find the optimal solution for the problem by genetic algorithm, the better and feasible solution is produced. Thus, this algorithm is efficient and can be applicable to practical problem. At last, an illustrative example is presented to show the application of the proposed algorithm.
引用
收藏
页码:3279 / 3301
页数:23
相关论文
共 19 条
  • [1] Afsarmanesh H, 1999, INT FED INFO PROC, V27, P127
  • [2] BARRY J, 1998, 2 IEEE INT ENT DISTR, P366
  • [3] Resource-constrained project scheduling: Notation, classification, models, and methods
    Brucker, P
    Drexl, A
    Mohring, R
    Neumann, K
    Pesch, E
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1999, 112 (01) : 3 - 41
  • [4] Camarinha-Matos LM, 1999, INT FED INFO PROC, V27, P3
  • [5] CHANGFENG Y, 2004, CHINA MECH ENG, V15, P414
  • [6] GRAVES RJ, 1996, IEEE CPMT INT EL MAN, P48
  • [7] Agile manufacturing: enablers and an implementation framework
    Gunasekaran, A
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 1998, 36 (05) : 1223 - 1247
  • [8] GENETIC SEARCH STRATEGIES IN MULTICRITERION OPTIMAL-DESIGN
    HAJELA, P
    LIN, CY
    [J]. STRUCTURAL OPTIMIZATION, 1992, 4 (02): : 99 - 107
  • [9] JUMIN H, 1998, J DALIAN U TECH, V38, P373
  • [10] Supply chain partnerships: Opportunities for operations research
    Maloni, MJ
    Benton, WC
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1997, 101 (03) : 419 - 429