DocumentCode :
3243892
Title :
Improved genetic algorithm for solving the fuzzy multiobjective Job Shop problem
Author :
Wang, He-Ping ; Shi, Lei
Author_Institution :
Anhui Univ. of Technol., Maanshan, China
fYear :
2010
fDate :
29-31 Oct. 2010
Firstpage :
1542
Lastpage :
1545
Abstract :
This paper studies the influence of the encoding and decoding on the result of the Job Shop problem under E/T indicators and improve the coding methods to make the optimal object span in order to adapt to different delivery windows earliness / tardiness scheduling problem. In this paper, The trapezoidal fuzzy number which has more representation as flexible operating processing time under fuzzy environment was used. Multi-attribute decision making method based on possibility was used. In this way it can reduce the intermediate process, avoid the loss of information, and enhance the effectiveness of fuzzy evaluation. Simulation results verify the effectiveness of the algorithm.
Keywords :
decision making; encoding; fuzzy set theory; genetic algorithms; job shop scheduling; problem solving; E/T indicator; decoding; delivery window earliness; encoding; flexible operating processing time; fuzzy evaluation; fuzzy multiobjective job shop problem; improved genetic algorithm; multiattribute decision making method; optimal object span; problem solving; tardiness scheduling; trapezoidal fuzzy number; Medical services; Fuzzy Multiobjective; Genetic Algorithm; Job Shop Scheduling; Multi-attribute Decision Making;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management (IE&EM), 2010 IEEE 17Th International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-6483-8
Type :
conf
DOI :
10.1109/ICIEEM.2010.5646109
Filename :
5646109
Link To Document :
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