用深度强化学习动态选择调度规则，提升作业车间调度效率
Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems
- 基于策略的强化学习动态切换调度规则，适应系统状态变化
- 在标准测试集上优于传统启发式与最新神经网络方法，降低完工时间
- 引入动作过滤和承诺机制，提升决策稳定性和评估准确性
本文提出一种基于策略的深度强化学习超启发式框架，用于求解作业车间调度问题。超启发式代理能够根据系统状态动态切换调度规则。我们扩展了该框架，引入两个关键机制：首先，动作预过滤将决策限制在可行的低层动作，使低层启发式可独立于环境约束进行评估，提供无偏判断；其次，承诺机制调控启发式切换频率。我们研究了不同承诺策略（从逐步切换到整轮承诺）对训练行为和完工时间的影响。此外，对比了两种策略层动作选择方式：确定性贪婪选择与随机采样。在标准JSSP基准测试上的计算实验表明，所提方法优于传统启发式、元启发式及近期基于神经网络的调度方法。
原文摘要 · Abstract (English)
This paper proposes a policy-based deep reinforcement learning hyper-heuristic framework for solving the Job Shop Scheduling Problem. The hyper-heuristic agent learns to switch scheduling rules based on the system state dynamically. We extend the hyper-heuristic framework with two key mechanisms. First, action prefiltering restricts decision-making to feasible low-level actions, enabling low-level heuristics to be evaluated independently of environmental constraints and providing an unbiased assessment. Second, a commitment mechanism regulates the frequency of heuristic switching. We investigate the impact of different commitment strategies, from step-wise switching to full-episode commitment, on both training behavior and makespan. Additionally, we compare two action selection strategies at the policy level: deterministic greedy selection and stochastic sampling. Computational experiments on standard JSSP benchmarks demonstrate that the proposed approach outperforms traditional heuristics, metaheuristics, and recent neural network-based scheduling methods
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