A scalable deep reinforcement learning approach for minimizing the total tardiness of the parallel machine scheduling problem

2024/10/30 by Li, Funing, Noortwyck, Ruben, Schulz, Robert
#Deep reinforcement learning #Dynamic parallel machine scheduling problem #Logistics scheduling #Recurrent neural network

paper · doi:10.2195/lj_proc_en_li_202410_01

Abstract

Various problems in the logistics field can be modeled as parallel machine scheduling problem (PMSP), which involves the optimized assignment of a set of jobs to a collection of parallel machines. Deep reinforcement learning (DRL) has demonstrated promising capability in solving similar problems. To this motivation, we propose a practical reinforcement learning-based framework to tackle a PMSP with new job arrivals and family setup constraints. We design a variable-length state matrix containing information of all jobs and employ a Recurrent Neural Network (RNN) model to represent the DRL agent. In the numerical experiment, we first train the agent on a small PMSP instance with 3 machines and 30 jobs. Then we implement this trained agent to solve a set of instances with significant larger instance. Its performance are also compared with two dispatching rules. The extensive experimental results demonstrate the scalability of our approach and its effectiveness across a variety of scheduling scenarios.

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