Scheduling with constraint programming in the dynamic and stochastic environment of a semiconductor photolithography area

This paper studies a photolithography scheduling problem with dynamic job arrivals, uncertain setup and processing times, and random machine breakdowns with stochastic downtimes. To address this real-life motivated complex problem, we adopt a rolling horizon approach, which involves solving a static and deterministic scheduling problem, executing a part of the proposed schedule, and rescheduling after a predetermined interval to create an updated schedule. To solve the static problem, a new constraint programming (CP) formulation is introduced. A case study is performed at a global semiconductor manufacturer in which a data-driven simulation model is used to accurately mimic the dynamics of the real-world photolithography area. In this simulation model, our proposed CP formulation is deployed with the rolling horizon approach and benchmarked against an alternative CP formulation from the recent literature, some well-known dispatching heuristics, and the current practice.