DocumentCode :
3044471
Title :
A Novel Two-Level Genetic Algorithm for Integrated Process Planning and Scheduling
Author :
Liang Wan ; Xinyu Li ; Liang Gao ; Xiaoyu Wen ; Wenwen Wang
Author_Institution :
State Key Lab. of Digital Manuf. Equip. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China
fYear :
2013
fDate :
13-16 Oct. 2013
Firstpage :
2790
Lastpage :
2795
Abstract :
Process planning and scheduling are two important sub-systems in modern manufacturing system. In manufacturing system, the two sub-systems of process planning and scheduling have been treated sequentially or separately in traditional methods. To increase the effectiveness of system performance, there is an increasing need for deep research and application of integrated process planning and scheduling (IPPS) system. In this paper, a novel two-level genetic algorithm (TGA) is proposed to optimize the IPPS problem. Based on the previous work, deep research should be made on the IPPS problem. In this study, the flow chart of TGA based on the previous integrated optimization strategy has been proposed. Experiment studies have been conducted to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm for the IPPS is a promising and very effective method.
Keywords :
genetic algorithms; process planning; scheduling; IPPS problem; IPPS system; TGA; integrated optimization strategy; integrated process planning and scheduling system; manufacturing system performance; two-level genetic algorithm; Biological cells; Genetic algorithms; Job shop scheduling; Optimization; Process planning; Sociology; Statistics; Process planning; improved genetic algorithm; integrated process planning and scheduling; scheduling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on
Conference_Location :
Manchester
Type :
conf
DOI :
10.1109/SMC.2013.476
Filename :
6722229
Link To Document :
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