Generating Training Data for Learning Linear Composite Dispatching Rules for Scheduling

A supervised learning approach to generating composite linear priority dispatching rules for scheduling is studied. In particular we investigate a number of strategies for generating training data for learning a linear dispatching rule using preference learning. The results show that generating training data set from optimal solutions only is not as effective as when suboptimal solutions are added to the set. Furthermore, different strategies for creating preference pairs is investigated as well as suboptimal solution trajectories. The different strategies are investigated on 2000 randomly generated problem instances using two different problems generator settings.

Citation

@incollection{InRu15a,
  year      = {2015},
  isbn      = {978-3-319-19083-9},
  booktitle = {Learning and Intelligent Optimization},
  volume    = {8994},
  series    = {Lecture Notes in Computer Science},
  editor    = {Dhaenens, Clarisse and Jourdan, Laetitia and Marmion, Marie-Eléonore},
  doi       = {10.1007/978-3-319-19084-6_22},
  title     = {Generating Training Data for Learning Linear Composite Dispatching Rules for Scheduling},
  url       = {http://dx.doi.org/10.1007/978-3-319-19084-6_22},
  publisher = {Springer International Publishing},
  author    = {Ingimundardóttir, Helga and Rúnarsson, Thomas Philip},
  pages     = {236-248},
  language  = {English}
}