Learning to Solve Job Shop Scheduling under Uncertainty
Guillaume Infantes Stéphanie Roussel Pierre Pereira Antoine Jacquet Emmanuel Benazera
Job-Shop Scheduling Problem (JSSP) is a combinatorial optimization problem
where tasks need to be scheduled on machines in order to minimize criteria such
as makespan or delay. To address more realistic scenarios, we associate a
probability distribution with the duration of each task. Our objective is to
generate a robust schedule, i.e. that minimizes the average makespan. This
paper introduces a new approach that leverages Deep Reinforcement Learning
(DRL) techniques to search for robust solutions, emphasizing JSSPs with
uncertain durations. Key contributions of this research include: (1)
advancements in DRL applications to JSSPs, enhancing generalization and
scalability, (2) a novel method for addressing JSSPs with uncertain durations.
The Wheatley approach, which integrates Graph Neural Networks (GNNs) and DRL,
is made publicly available for further research and applications.