Solving the Mask Data Preparation Scheduling Problem Using Meta-Heuristics

Abstract

Mask data preparation (MDP) is a part of the mask data process for fabricating semiconductors, and its importance has commonly been neglected. This paper proposes an integer linear programming model and two meta-heuristics, a genetic algorithm (GA) and simulated annealing (SA), for solving the MDP scheduling problem (MDPSP). The proposed meta-heuristics are empirically evaluated using 768 simulation instances of MDPSP based on the characteristics of a real technology company and compared with the most commonly used first-come, first-served method. The experimental results reveal that the proposed GA and SA algorithms can critically improve the manufacturing schedule for semiconductor factories.INDEX TERMS Scheduling, integer linear programming, mask data preparation, meta-heuristics, genetic algorithm, simulated annealing.