Structured learning based heuristics to solve the single machine scheduling problem with release times and sum of completion times

In this paper, we focus on the solution of a hard single machine scheduling problem by new heuristic algorithms embedding techniques from machine learning and scheduling theory. These heuristics use a dedicated predictor to 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. We introduce a structured learning approach which enables to fit the predictor using a database of instances with their optimal solution. Computational experiments show that the proposed learning based heuristics are competitive with state-of-the-art heuristics, notably on large instances for which they provide the best results.