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Design of a neurofuzzy–regression expert system to estimate cost in a flexible jobshop automated manufacturing system
 Hamed Fazlollahtabar,
 Nezam MahdaviAmiri
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Abstract
We propose a cost estimation model based on a fuzzy rule backpropagation network, configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach, we determine the optimal path of the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty.
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 Title
 Design of a neurofuzzy–regression expert system to estimate cost in a flexible jobshop automated manufacturing system
 Journal

The International Journal of Advanced Manufacturing Technology
Volume 67, Issue 58 , pp 18091823
 Cover Date
 20130701
 DOI
 10.1007/s0017001246105
 Print ISSN
 02683768
 Online ISSN
 14333015
 Publisher
 Springer London
 Additional Links
 Topics
 Keywords

 Automated manufacturing system
 Cost estimation
 Neural network
 Fuzzy logic
 Regression analysis
 Industry Sectors
 Authors

 Hamed Fazlollahtabar ^{(1)}
 Nezam MahdaviAmiri ^{(2)}
 Author Affiliations

 1. Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
 2. Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran