期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 34, 期 3, 页码 1680-1691出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2007.01.031
关键词
supply chain management; agent-based; fuzzy time series; bullwhip effect; simulation
This paper addresses the bullwhip effect in a multi-stage supply chain, where all demands, lead times, and ordering quantities are fuzzy. To simulate the bullwhip effect, a modified Hong Fuzzy Time Series is presented by adding a Genetic Algorithm (GA) module for gaining of a window basis. Next, a back propagation neural network is used for defuzzification. The model can forecast the trends in fuzzy data. Then, an agent-based system is developed to minimize the total cost and to reduce the bullwhip effect in supply chains. The system can suggest the reasonable ordering policies. The results show that the propose system is superior than the previous analytical methods in terms of discovering the best available ordering policies. (C) 2007 Elsevier Ltd. All rights reserved.
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