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