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, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported in literature to provide feasible solutions with reasonable CPU times.This work introduces an improvement MILP-based decomposition strategy that combines the benefits of a rigorous continuous-time MILP (mixed integer linear programming) formulation with the flexibility of heuristic procedures. The schedule generated provides enhanced solutions over time to challenging real-world automated wet etch station scheduling problems with moderate computational cost. This methodology was able to provide more than a 7% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed.

► An improvement MILP-based decomposition strategy combining a rigorous continuous-time MILP formulation with an efficient decomposition procedure is introduced.
► The proposed strategy lies on an exact method to iteratively generate and improve a detailed schedule of production activities and transfer operations.
► This work provides a robust and very flexible strategy for the solution of challenging industrial-scale scheduling problems.