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

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.