Decentralized Genetic Algorithm-Based Online Rescheduling for Semiconductor Manufacturing Systems

For semiconductor manufacturing factories, the creation of a master schedule spanning several weeks is crucial for outlining the production timeline, including processing schedules and lot locations. However, the semiconductor assembly line operates within a dynamic environment that is subject to random disruptions, such as machine failures. These disruptions can lead to delays and time window violation, thereby rendering the master schedule suboptimal or even infeasible. This paper specifically addresses the impact of long machine failures, defined as those exceeding an hour, which significantly disrupt the assembly line’s operations. To confront this challenge, we propose a decentralized genetic algorithm tailored to adjust a short-term master schedule while accommodating re-entrant operations and adhering to time window constraints. Our experiments are conducted using real-world datasets, and the results illustrate that the proposed method effectively reduces delays and prevents time window violations within limited computational time.