An improvement-based MILP optimization approach to complex AWS scheduling

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. Due to its inherent complexity, real-world automated wet-etch station scheduling problems are very difficult to solve using traditional mathematical formulations. Thus, heuristic, metaheuristics and simulation-based methods have been reported in literature to provide feasible solutions with reasonable CPU times. This work presents a novel hybrid MILP-based decomposition strategy that combines the benefits of a rigorous MILP (Mixed Integer Linear Programming) continuous-time formulation with the flexibility of dynamic heuristic procedures. The schedule generated provides near-optimal dynamic solutions to challenging industrial-sized automated wet-etch station scheduling problems with moderate computational cost. Also, this methodology provides more than a 10% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed.