Introducing PetriRL: An Innovative Framework for JSSP Resolution
  Integrating Petri nets and Event-based Reinforcement Learning
Sofiene Lassoued Andreas Schwung
Quality scheduling in industrial job shops is crucial. Although neural
networks excel in solving these problems, their limited explainability hinders
their widespread industrial adoption. In this research, we introduce an
innovative framework for solving job shop scheduling problems (JSSP). Our
methodology leverages Petri nets to model the job shop, not only improving
explainability but also enabling direct incorporation of raw data without the
need to preprocess JSSP instances into disjunctive graphs. The Petri net, with
its controlling capacities, also governs the automated components of the
process, allowing the agent to focus on critical decision-making, particularly
resource allocation. The integration of event-based control and action masking
in our approach yields competitive performance on public test benchmarks.
Comparative analyses across a wide spectrum of optimization solutions,
including heuristics, metaheuristics, and learning-based algorithms, highlight
the competitiveness of our approach in large instances and its superiority over
all competitors in small to medium-sized scenarios. Ultimately, our approach
not only demonstrates a robust ability to generalize across various instance
sizes but also leverages the Petri net's graph nature to dynamically add job
operations during the inference phase without the need for agent retraining,
thereby enhancing flexibility.