Artificial neural networks for design of manufacturing systems and selection of priority rules

Abstract

Simulation is one of the most effective methods in the Design of Manufacturing Systems (MS). Typical reasons for simulation of a manufacturing system includes evaluating the capacity and equipment utilization, identifying bottlenecks in the system, comparing the performance of alternative designs. Simulation is often coupled with Artificial Intelligence (AI) techniques to provide an efficient decision making framework. In this study, Artificial Neural Networks (ANN) are used for the design of a manufacturing system. Four different priority rules are used: EDD, SPT, CR and FCFS. As a result four different design alternatives are obtained from trained ANN. Performance measures of a manufacturing system are given to the ANN which then gives a design alternative. The design alternatives are evaluated in terms of performance measures and then the best design alternative is selected from four different alternatives.