Simulation-based performance assessment of master planning approaches in semiconductor manufacturing

• A simulation framework that allows for modeling the demand and the production system is proposed.
• A performance assessment methodology for production planning approaches is suggested.
• Two heuristics for master planning are assessed within a rolling horizon approach.
• Demand and execution uncertainty are considered in the rolling horizon setting.
• A reduced discrete-event simulation model is used to mimic a one-stage network of wafer fabs.

Semiconductor manufacturing is confronted with a large number of products whose mix is changing over time, heterogeneous fabrication processes, re-entrant flows of material, and different sources of environmental and system uncertainty. In this context, the mid-term production planning approach, i.e., master planning, typically does not capture the entire complexity of the shop-floor. It deals with an aggregated representation of the production system. There is a need for evaluating the planning algorithm in use while taking the execution level into account. Therefore, we introduce in this paper a simulation-based framework that allows for modeling the behavior of the market demand and the production system. An appropriate performance assessment methodology is proposed. The performance of two heuristic approaches for master planning in semiconductor manufacturing, a genetic algorithm and a rule-based assignment procedure, is evaluated within a rolling horizon setting while considering demand and execution uncertainty. A reduced discrete-event simulation model is used to mimic a one-stage network of wafer fabrication facilities. The results of simulation experiments are discussed.