A Novel MILP-based Strategy to Industrial Scheduling Problems in Semiconductor Manufacturing Systems

AGUIRRE, ADRIÁN MARCELO; MÉNDEZ, CARLOS ALBERTO

Buenos Aires

Workshop; ALIO/INFORMS International Meeting; 2010

Universidad de Buenos Aires

The Automated Wet-etch Station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. In this area, real-world AWS scheduling problems are very difficult to solve to optimality using traditional approaches like exact methods. In this way, heuristic and meta-heuristics methods have been reported in the literature to provide feasible solutions with reasonable CPU times.This work presents a novel hybrid MILP-based decomposition strategy that joins the advantages of a robust MILP continuous time formulation with the flexibility of dynamic heuristic procedures. The schedule generated provides near-optimal dynamic solutions to challenging industrial-sized AWS scheduling problems.