Learning-based scheduling of flexible manufacturing systems using case-based reasoning

Abstract

A common method of dynamically scheduling jobs in Flexible Manufacturing Systems (FMSs) is to employ dispatching rules. However, the problem associated with this method is that the performance of the rules depends on the state of the system, but there is no rule that is superior to all the others for all the possible states the system might be in. It would therefore be highly desirable to employ the most suitable rule for each particular situation. To achieve this, this paper presents a scheduling approach that uses Case-Based Reasoning (CBR), which analyzes the system's previous performance and acquires "scheduling knowledge," which determines the most suitable dispatching rule at each particular moment in time. Simulation results indicate that the proposed approach produces significant performance improvements over existing dispatching rules.