Transformer-Based Flow Shop Scheduling Using MILP-Generated Training Data
The study concludes that transformer models can, to some extent, learn patterns from MILP-optimized non-permutation flow shop schedules and that transformer-based scheduling represents an interesting direction for future research, particularly in settings with a fixed, recurring job set.
Roderich Wallrath
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