Learning to Schedule Machines and Operators: A Human-Aware Deep Q-Learning Framework for Dynamic Flexible Job Shops
TL;DR
A human-centered Deep Reinforcement Learning framework, DD4LQN, for dynamic flexible job shop scheduling under operator learning and forgetting dynamics, which integrates a disturbance-aware scenario generator, bounded logistic learning-forgetting dynamics, and deterministic Dual Pair Ranking to ensure auditable decisions.
Abstract
Most learning-based schedulers for job shop problems assume static workforce performance. However, dynamic dual-resource job shops must navigate stochastic disturbances and time-varying task durations due to human factors. This study proposes a human-centered Deep Reinforcement Learning framework, DD4LQN, for dynamic flexible job shop scheduling under operator learning and forgetting dynamics. The environment integrates a disturbance-aware scenario generator, bounded logistic learning-forgetting dynamics, and deterministic Dual Pair Ranking to ensure auditable decisions. The training used a plan that included ENTRY and EXIT greedy evaluations and a Quote-then-Commit process. Tests over ten weeks showed that DD4LQN-EXIT did better than other scheduling methods. Empirical evaluations over a ten-week horizon demonstrate that DD4LQN-EXIT got the average schedule reward of 0.5843. It was better than Earliest Due Date (EDD) and Shortest Processing Time (SPT) by 17% and 14%, respectively. It also completed jobs and had fewer risky orders. Even though Shortest Processing Time had delays on average, it completed fewer jobs because it focused on short tasks. Additionally, an exploratory ablation across the same scenarios demonstrated that Dual Pair Rank and learning–forgetting dynamics come up with better results. Furthermore, representation diagnostics on the 227,717-parameter network confirm structural stability, with layer matrices retaining up to 98.2% of maximum effective rank without capacity collapse.