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In this paper, we focus on the solution of a hard single machine schedulingproblem by new heuristic algorithms embedding techniques from machine learningfield and scheduling theory. These heuristics transform an instance of the hardproblem into an instance of a simpler one solved to optimality. The obtainedschedule is then transposed to the original problem. Computational experimentsshow that they are competitive with state-of-the-art heuristics, notably onlarge instances.

Learning to solve the single machine scheduling problem with release times and sum of completion times