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