Evolving priority rules for on-line scheduling of jobs on a single machine with variable capacity over time

On-line scheduling is often required in a number of real-life settings. This is the case of distributing charging times for a large fleet of electric vehicles arriving stochastically to a charging station working under power constraints. In this paper, we consider a scheduling problem derived from a situation of this type: one machine scheduling with variable capacity and tardiness minimization, denoted (1, Cap(t)parallel to Sigma T-i). The goal is to develop new priority rules to improve the results from some classical ones as Earliest Due Date (EDD) or Apparent Tardiness Cost (ATC). To this end, we developed a Genetic Programming (GP) approach. The efficiency of this algorithm relies on some smart representation of the expression trees. Besides, we restrict the search space to that of dimensionally compliant expressions, which allows GP to reach single and clear solutions. We conducted an experimental study showing that GP is able to evolve new rules that outperform ATC and EDD using the same problem attributes and operations. (C) 2019 Elsevier B.V. All rights reserved.