SEVAL, a learned scheduler that evaluates whole sets of job-machine assignments at once, reports mean optimality gaps of 6.5% on Taillard and 9.9% on Demirkol, ahead of prior deep learning schedulers.
Benchmarks for shop scheduling problems
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Self-Evaluation for Job-Shop Scheduling
SEVAL, a learned scheduler that evaluates whole sets of job-machine assignments at once, reports mean optimality gaps of 6.5% on Taillard and 9.9% on Demirkol, ahead of prior deep learning schedulers.