Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to
  Sequence approach
Giovanni Bonetta Davide Zago Rossella Cancelliere Andrea Grosso
Job scheduling is a well-known Combinatorial Optimization problem with
endless applications. Well planned schedules bring many benefits in the context
of automated systems: among others, they limit production costs and waste.
Nevertheless, the NP-hardness of this problem makes it essential to use
heuristics whose design is difficult, requires specialized knowledge and often
produces methods tailored to the specific task. This paper presents an original
end-to-end Deep Reinforcement Learning approach to scheduling that
automatically learns dispatching rules. Our technique is inspired by natural
language encoder-decoder models for sequence processing and has never been
used, to the best of our knowledge, for scheduling purposes. We applied and
tested our method in particular to some benchmark instances of Job Shop
Problem, but this technique is general enough to be potentially used to tackle
other different optimal job scheduling tasks with minimal intervention. Results
demonstrate that we outperform many classical approaches exploiting priority
dispatching rules and show competitive results on state-of-the-art Deep
Reinforcement Learning ones.