Fast scheduling of semiconductor manufacturing facilities using case-based reasoning

Abstract

This paper presents a scheduling method for semiconductor manufacturing systems through utilizing a case-based reasoning approach that consists of modeling, casebase building, and reasoning steps. Petri nets are employed as a model for representing the behavior of a system and accommodating various types of constraints. Casebase contains the cases, composed of pairs of a state and the decision made at the state, and it is built from the solutions of previously solved problems. Subsequently, a schedule for a new problem with different production requirements, initial setup status, and number of setup change operators is obtained by sequentially retrieving and reusing the cases that are most similar to the states encountered during schedule generation in the reasoning step. Extensive experiments demonstrate the performances of the proposed approach, compared to those of dispatching rule and meta-heuristic methods, and also show the robustness against possible perturbations in terms of production requirements and initial setup status based on real world scale datasets. The proposed method significantly outperformed a meta-heuristic algorithm in terms of computation time while requiring much less sacrifice in a performance metric than the competitive dispatching rules.