A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups

As an essential scheduling problem with several practical applications, the parallel machine scheduling problem (PMSP) with family setups constraints is difficult to solve and proven to be NP-hard. To this end, we present a deep reinforcement learning (DRL) approach to solve a PMSP considering family setups, aiming at minimizing the total tardiness. The PMSP is first modeled as a Markov decision process, where we design a novel variable-length representation of states and actions, so that the DRL agent can calculate a comprehensive priority for each job at each decision time point and then select the next job directly according to these priorities. Meanwhile, the variable-length state matrix and action vector enable the trained agent to solve instances of any scales. To handle the variable-length sequence and simultaneously ensure the calculated priority is a global priority among all jobs, we employ a recurrent neural network, particular gated recurrent unit, to approximate the policy of the agent. The agent is trained based on Proximal Policy Optimization algorithm. Moreover, we develop a two-stage training strategy to enhance the training efficiency. In the numerical experiments, we first train the agent on a given instance and then employ it to solve instances with much larger scales. The experimental results demonstrate the strong generalization capability of the trained agent and the comparison with three dispatching rules and two metaheuristics further validates the superiority of this agent.

Automated summary

This article proposes a deep reinforcement learning approach to solve the parallel machine scheduling problem with family setups constraints, aiming to minimize the total tardiness. By designing a novel variable-length representation of states and actions, the method can calculate a comprehensive priority for each job at each decision time point and select the next job directly according to these priorities. The experimental results demonstrate the strong generalization capability of the trained agent and validate its superiority compared to three dispatching rules and two metaheuristics.