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Design of a neuro-fuzzy–regression expert system to estimate cost in a flexible jobshop automated manufacturing system

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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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Authors and Affiliations

  1. Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

    Hamed Fazlollahtabar

  2. Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran

    Nezam Mahdavi-Amiri

Authors
  1. Hamed Fazlollahtabar
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  2. Nezam Mahdavi-Amiri
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Correspondence to Hamed Fazlollahtabar.

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Fazlollahtabar, H., Mahdavi-Amiri, N. Design of a neuro-fuzzy–regression expert system to estimate cost in a flexible jobshop automated manufacturing system. Int J Adv Manuf Technol 67, 1809–1823 (2013). https://doi.org/10.1007/s00170-012-4610-5

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  • DOI: https://doi.org/10.1007/s00170-012-4610-5

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