Dynamic dispatching for interbay automated material handling with lot targeting using improved parallel multiple-objective genetic algorithm

With the growth of wafer size from 200 mm to 300 mm and then to 450 mm in recent years, automatic material handling system (AMHS) has played an indispensable role in semiconductor wafer fabrication systems, and improving the overall efficiency of interbay AMHS has therefore received considerable attention. This study investigates the integrated scheduling problem in an interbay AMHS that combines vehicle scheduling with lot targeting. However, the large-scale, dynamic, and stochastic production environment significantly substantiates the complexity of the scheduling problem. To meet the demands of adaptive adjusting, efficient scheduling, and multiple-objective optimization, this study develops an improved parallel multiple-objective genetic algorithm with full use of parallel strategy, multiobjective evolutionary process, and local search strategy. Simulation experiments have been conducted and the numerical results illustrate the superiority of the algorithm in terms of comprehensive performance of multiple sub-objectives. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study focuses on improving the efficiency of interbay AMHS in semiconductor wafer fabrication systems, and proposes an improved parallel multiple-objective genetic algorithm solution that utilizes parallel strategy, multiobjective evolutionary process, and local search strategy. Simulation experiments demonstrate the superiority of the algorithm in terms of comprehensive performance of multiple sub-objectives.