DocumentCode :
2376607
Title :
A grey genetic algorithm for uncertainty reverse logistics
Author :
Chen-Fang Tsai
Author_Institution :
Dept. of Ind. Manage. & Enterprise Inf., Aletheia Univ., Tainan, Taiwan
fYear :
2012
fDate :
23-25 May 2012
Firstpage :
885
Lastpage :
892
Abstract :
This study presents the uncertainty remanufacturing demand (URD) for green suitcase chain to predict the return demand model of an flexible inventory model. In this research, we proposed reverse production design by green prediction model for the division of green cost responsibility. This philosophy have become a popular topic that improved the effectiveness of extended producer responsibility for green supply chain management (GSCM). It is different from the traditional division of green cost responsibility processes that we proposed a novel measurement by the uncertainty index from green prediction model evolutions. A grey genetic algorithm (GGA) was designed by adaptive designs for URD optimization. These designs provided a novel evaluation index by varying all variables to achieve the global optimization of green cost responsibility. The new demand prediction design derived from the crossover and mutation rate of an adjusted GA search optimization. This research verified these methodologies in a practical case. The experiment is simulated by a GGA to reach an optimal solution.
Keywords :
costing; environmental economics; genetic algorithms; grey systems; process design; recycling; reverse logistics; stock control; supply chain management; GA search optimization; GSCM; URD optimization; green cost responsibility; green prediction model; green suitcase chain; green supply chain management; grey genetic algorithm; inventory model; return demand model; reverse production design; uncertainty remanufacturing demand; uncertainty reverse logistics; Logistics; Optimization; Uncertainty; Customer-Design Product; Green Product Prediction Model; Grey Genetic Algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Supported Cooperative Work in Design (CSCWD), 2012 IEEE 16th International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4673-1211-0
Type :
conf
DOI :
10.1109/CSCWD.2012.6221926
Filename :
6221926
Link To Document :
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