Job Shop Scheduling: A Novel DRL approach for continuous schedule-generation facing real-time job arrivals

Abstract

We present a DRL-based novel approach to solve the Job Shop Scheduling Problem (JSSP) in real-time while facing unpredictable job arrival disruptions. Our proposed approach consists of continuously generating improved schedules based on a rescheduling technique: It leads to continuous generation of schedules in triggered rescheduling points, and thus gives immediate response to random job arrivals. To implement the proposed technique, we use Proximal Policy Optimization Actor and Critic (PPO-AC), a combination of two RL algorithms. PPO-AC is used to assign job operations to available machines based on the job shop state that is represented by dynamic disjunctive graphs, modeling precedence constraints between job operations, and resource sharing constraints. Graph Embedding modeling is also applied for dynamic graph representation in PPO-AC algorithm. Preliminary numerical experiments of our innovative solution are discussed in this paper.