General Particle Swarm Optimization Algorithm for Integration of Process Planning and Scheduling

被引：0
|
作者：
Xu Shaotan [1 ]
Li Xinyu [1 ]
Gao Liang [1 ]
Sun Yi [1 ]
机构：
[1] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China
来源：
MATERIALS AND PRODUCT TECHNOLOGIES | 2010年 / 118-120卷
关键词：
Particle swarm optimization; Integration of process planning and scheduling; Tabu search;
D O I：
10.4028/www.scienqfic.net/AMR.118-120.409
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
To realize the integration of process planning and scheduling (IPPS) in the manufacturing system, a particle swarm optimization (PSO) algorithm is utilized. Based on the general PSO (GPSO) model, one GPSO algorithm is projected to solve IPPS. In GPSO, crossover and mutation operations of genetic algorithm are respectively used for particles to exchange information and search randomly, and tabu search (TS) is used for particles' local search. And time varying crossover probability and time varying maximum step size of tabu search are introduced. Experimental results show that IPPS can be solved by GPSO effectively. The feasibility of the proposed GPSO model and the significance of the research on IPPS are also demonstrated.
引用
收藏
页码：409 / 413
页数：5
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