Genetic programming to evolve priority rules for on-line scheduling on single machine with variable capacity

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 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)|| ∑ Ti). 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. We conducted an experimental study showing that it is possible to evolve new efficient rules that outperform ATC and EDD using the same problem attributes and operations.

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