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Design of an expert system to estimate cost in an automated jobshop manufacturing system

Fazlollahtabar, H ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1109/ICCIE.2010.5668385
  3. Publisher: 2010
  4. Abstract:
  5. We propose a cost estimation model based on a fuzzy rule back-propagation network (BPN), 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 in 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
  6. Keywords:
  7. Neural network ; Regression analysis ; Automated manufacturing systems ; Backpropagation network ; Cost estimation ; Cost estimation models ; Cost estimations ; Estimate cost ; Job-shop ; Manufacturing networks ; Manufacturing system ; Multiple linear regression analysis ; Numerical example ; Optimal paths ; Automation ; Costs ; Dense wavelength division multiplexing ; Dynamic programming ; Estimation ; Expert systems ; Fuzzy logic ; Fuzzy systems ; Industrial engineering ; Linear regression ; Manufacture ; Metal analysis ; Neural networks ; Production engineering ; Soft computing ; Statistics ; Uncertainty analysis ; Cost benefit analysis
  8. Source: 40th International Conference on Computers and Industrial Engineering: Soft Computing Techniques for Advanced Manufacturing and Service Systems, CIE40 2010, 25 July 2010 through 28 July 2010, Awaji ; 2010 ; 9781424472956 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5668385/?reload=true