ACO-based scheduling of parallel batch processing machines to minimize the total weighted tardiness

Abstract

This research was motivated by the scheduling problem of parallel batch processing machines located in the diffusion and oxidation areas in a semiconductor wafer fabrication facility (wafer fab). The objective was to minimize the total weighted tardiness (TWT) on parallel batch processing machines which have incompatible job families, dynamic job arrivals, and constraints on the sequence-dependent setup time and the qualification-run requirements of advanced process control. Since the problem is NP-hard, an ant colony optimization (ACO) algorithm was used to achieve a satisfactory solution in a reasonable computation time. Extensive simulation experiments had been studied to demonstrate the effectiveness of the proposed method. The simulation results showed that the proposed ACO algorithm is superior to a modified Apparent Tardiness Cost-Batched Apparent Tardiness Cost rule adapted to dynamic job arrivals for minimizing the TWT. More machines or the bigger capacity, better improvement of the TWT is achieved. In addition, more machines, jobs or recipes require longer computation time, while bigger capacity requires less computation time.