A hybrid surrogate-assisted dual-population co-evolutionary algorithm for multi-area integrated scheduling in wafer fabs

Abstract

In wafer fabrication, multiple areas handle different processes and production flows. To maintain the desired chemical and physical properties of wafers, strict time window constraints (TWCs) must be observed as wafers progress through these areas. However, independent scheduling within each area without collaboration complicates resource allocation and hinders overall production optimization. Implementing multi-area integrated scheduling is thus essential for effective production management, aiming to reduce total lead time and production costs. This paper proposes a hybrid surrogate-assisted dual-population co-evolutionary algorithm (HSA-DPEA) to efficiently tackle the multi-area integrated scheduling problem under multiple TWCs. The algorithm employs a dual-population co-evolutionary mechanism, consisting of normal and auxiliary populations, to balance convergence and diversity while ensuring feasibility. The normal population focuses on feasible solutions to maintain overall quality, while the auxiliary population explores infeasible regions to identify promising individuals that can guide the normal population's evolution. To enhance evolutionary efficiency and reduce the number of time-consuming real fitness evaluations, a hybrid surrogate-assisted model is introduced. This model adapts by training regression or classification models at different stages of population evolution. Additionally, an online learning strategy based on convergence and diversity is employed for continuous model updating to improve accuracy. The proposed algorithm is tested on 18 instances and validated through six months of continuous testing on a wafer fab simulation system. The results demonstrate that HSA-DPEA obtains better Pareto optimal sets, effectively reducing total lead time and production costs in multi-area integrated scheduling under multiple TWCs.