Learning to solve the single machine scheduling problem with release times and sum of completion times
This work provides competitive heuristic solutions for the single machine scheduling problem with release times and sum of completion times, which is relevant for practitioners in operations research and logistics.
This paper addresses a challenging single machine scheduling problem by developing new heuristic algorithms. These heuristics convert the complex problem into a simpler, optimally solvable one, and then adapt the solution back to the original problem. The computational experiments demonstrate that these algorithms are competitive with existing state-of-the-art heuristics, especially for larger problem instances.
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.