DocumentCode :
1871269
Title :
Evolutionary computation on multicriteria production process planning problem
Author :
Zhou, Gengui ; Gen, Mitsuo
Author_Institution :
Dept. of Ind. & Syst. Eng., Ashikaga Inst. of Technol., Japan
fYear :
1997
fDate :
13-16 Apr 1997
Firstpage :
419
Lastpage :
424
Abstract :
The production process planning (PPP) problem is abundant among manufacturing systems. In general the problem can be approached by network analysis or dynamic programming. It is difficult for traditional optimization techniques to cope with the multicriteria production process planning (mPPP) problem. In this paper, a new evolutionary computation (EC) approach is developed to deal with the PPP problems with both single or multiple objective criteria. The proposed EC approach adopts a new simple state permutation encoding and combines with the neighborhood search technique in mutation operation to improve the evolutionary process in finding the optimal solution of the PPP problems. The numerical analysis shows that the proposed EC is both effective and efficient for the PPP problems
Keywords :
dynamic programming; genetic algorithms; manufacturing resources planning; operations research; production control; search problems; dynamic programming; evolutionary computation; manufacturing systems; multicriteria production process planning problem; multiple objective criteria; mutation operation; neighborhood search technique; network analysis; numerical analysis; optimal solution; optimization; simple state permutation encoding; single objective criteria; Dynamic programming; Encoding; Evolutionary computation; Genetic mutations; Manufacturing industries; Manufacturing processes; Process planning; Production systems; Systems engineering and theory; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 1997., IEEE International Conference on
Conference_Location :
Indianapolis, IN
Print_ISBN :
0-7803-3949-5
Type :
conf
DOI :
10.1109/ICEC.1997.592347
Filename :
592347
Link To Document :
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