Scheduling with Partial Job Size Predictions: Algorithms and Competitive Ratios

The authors investigate the non-clairvoyant scheduling problem where the decision-maker has access to the exact sizes of only a subset of the jobs. They establish near-optimal lower bounds and algorithms for the case of perfect predictions, and introduce a learning-augmented algorithm that exhibits a novel tradeoff between consistency and smoothness when the number of predictions is limited.