Sequence representation based adaptive large neighbourhood search for online rescheduling of semiconductor manufacturing back-end assembly systems

In semiconductor manufacturing assembly systems, a master schedule spanning several weeks outlines the production timeline. However, unexpected events like random machine failures can disrupt this schedule, causing sequencing delays and time window violations. These disruptions often render the master schedule suboptimal or infeasible, resulting in productivity losses. Remaking a new schedule is time-consuming and may lead to greater losses than the initial disruption. To address this challenge, we propose a novel Adaptive Large Neighbourhood Search (ALNS) method for rapid rescheduling. This approach accounts for complex re-entrant flows and time window constraints. Unlike traditional methods using disjunctive graph representations, the proposed ALNS employs sequence representation to mitigate infeasibility caused by machine failures. We also introduce specialised destroy and repair operators tailored to this problem context. The objective is to minimise deviations from the original schedule, making it a practical solution for real-world applications. Experimental results demonstrate that our method significantly reduces delays and time window violations, effectively enhancing system resilience.