Attribute Selection for the Scheduling of Flexible Manufacturing Systems Based on Fuzzy Set-theoretic Approach and Genetic Algorithm

Assigning proper dispatching rules dynamically has been shown to enhance various performance measures for a flexible manufacturing system (FMS). To achieve this, real-time salient information of the system is extracted and then a rule’s dispatching mechanism is built for the scheduling task. For a dynamic scheduled FMS, two critical issues dominate the performance; the first is the selection of system attributes and the second is the design of the dispatching mechanism. This paper aims to deal with the first issue. A good attribute evaluation method should provide the information from which attribute are selected or removed. In this paper, a supervised attribute mining algorithm (SAMA), which is based on the fuzzy set-theoretic approach and genetic algorithm (GA), is proposed to execute this function. SAMA is able to rank attributes according to their relative importance. In the experiment, a FMS is conducted to demonstrate the validity of the proposed SAMA. The experimental results indicate that the attribute evaluation task and optimal attribute subset selection can be achieved by using the SAMA. Moreover, compared with using all system attributes without selection, performance of the FMS can be improved by using the optimal attributes as input of the scheduler.