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The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasksthat specifically consider the inherent uncertainties such as changing orderrequirements and possible machine breakdown in realistic smart manufacturingsettings. Since traditional methods cannot dynamically generate effectivescheduling strategies in face of the disturbance of environments, we formulatethe DJSP as a Markov decision process (MDP) to be tackled by reinforcementlearning (RL). For this purpose, we propose a flexible hybrid framework thattakes disjunctive graphs as states and a set of general dispatching rules asthe action space with minimum prior domain knowledge. The attention mechanismis used as the graph representation learning (GRL) module for the featureextraction of states, and the double dueling deep Q-network with prioritizedreplay and noisy networks (D3QPN) is employed to map each state to the mostappropriate dispatching rule. Furthermore, we present Gymjsp, a publicbenchmark based on the well-known OR-Library, to provide a standardizedoff-the-shelf facility for RL and DJSP research communities. Comprehensiveexperiments on various DJSP instances confirm that our proposed framework issuperior to baseline algorithms with smaller makespan across all instances andprovide empirical justification for the validity of the various components inthe hybrid framework.

Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism