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Producer's behavior analysis in an uncertain bicriteria AGV-based flexible jobshop manufacturing system with expert system

Fazlollahtabar, H ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1007/s00170-012-4283-0
  3. Publisher: 2013
  4. Abstract:
  5. Here, an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost is proposed. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, time is considered to be a triangular fuzzy number and apply an expert system to infer the cost. The expert system based on fuzzy rule backpropagation network to configure the rules for estimating the cost under uncertainty is proposed. A multiple linear regression model is applied to analyze the rules and find the effective rules for cost estimation. The objective is to find a path minimizing an aggregate weighted unscaled time and cost criteria. A fuzzy dynamic programming approach is presented for computing a shortest path in the network. Then, a comprehensive economic and reliability analysis is worked out on the obtained paths to find the optimal producer's behavior. Finally, an application of the model is illustrated by a numerical example. The results show the effectiveness of our approach for finding an optimal path in a manufacturing system under uncertainty
  6. Keywords:
  7. Producer's behavior ; Automated guided vehicle (AGV) ; Flexible jobshop ; Fuzzy systems ; Automated guided vehicles ; Backpropagation network ; Flexible job shops ; Fuzzy dynamic programming ; Manufacturing networks ; Multiple linear regression models ; Expert system ; Triangular fuzzy numbers ; Expert systems ; Linear regression ; Manufacture ; Mobile robots ; Optimization ; Reliability analysis ; Uncertainty analysis ; Cost estimating
  8. Source: International Journal of Advanced Manufacturing Technology ; 2013 , Pages 1605-1618 ; 02683768 (ISSN)
  9. URL: http://link.springer.com./article/10.1007%2Fs00170-012-4283-0