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

TL;DR

This paper introduces machine learning-based heuristics for solving complex single machine scheduling problems with release times, transforming difficult instances into simpler ones for optimal solutions and demonstrating competitive performance on large instances.

Contribution

It presents novel heuristic algorithms that embed machine learning techniques to effectively solve hard scheduling problems by instance transformation.

Findings

Heuristics are competitive with state-of-the-art methods.

Effective on large problem instances.

Transformations lead to optimal solutions for simplified problems.

Abstract

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.