Metaheuristics Approach for Rule Acquisition in Flexible Shop Scheduling Problems
In this paper, we deal with an extended class of flexible shop scheduling problems. A solution is composed under the condition where information on jobs to be processed may not be given beforehand, i.e., under the framework of real-time scheduling. To realize a solution, we apply such a method where jobs are to be dispatched by applying a set of rules (a rule-set), and propose an approach in which rule-sets are generated and improved by using the genetics-based machine learning technique. Through some computational experiments, the effectiveness and the potential of the proposed approach are investigated.
KeywordsRule acquisition genetics-based machine learning flexible shop scheduling problem simulation
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