RIDRL: A Deep Reinforcement Learning Based on Multiple Dispatching Rules and IGA Algorithm for JSP

Su Jin, Lyu Shubin, Lu Xin, Wan You, Liao Fusheng
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Abstract

Job shop scheduling problem (JSP) is a very general and complex scheduling problem in the manufacturing industry. The traditional priority dispatching rule (PDR) can get the approximate solution for some specific problems. Nevertheless, for the complex and changing realistic factory floor, the quality of the existing solution fluctuates significantly. To solve the problem, this paper fuse multiple dispatching rules and the Insertion Greedy Algorithm (IGA) to deep reinforcement learning (DRL), namely RIDRL, to solve the job shop scheduling problem. In this method, we manually choose five generalizable state features as the states of the workshop environment. Employing 18 scheduling rules as the action space in the agent while designing a quick converge reward function. Additionally, we use a Proximal Policy Optimization Algorithm (PPO) to train the DRL agent with minimizing makespan as the optimization objective. Several simulation experiments on many standard instances indicate that the proposed method obtains competitive solutions for problems of different sizes.
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RIDRL:基于多调度规则和IGA算法的JSP深度强化学习
作业车间调度问题(Job shop scheduling problem, JSP)是制造业中一个非常普遍和复杂的调度问题。传统的优先级调度规则(PDR)可以得到一些具体问题的近似解。然而,对于复杂和不断变化的现实工厂车间,现有解决方案的质量波动很大。为了解决这一问题,本文将多调度规则和插入贪婪算法(IGA)融合到深度强化学习(DRL)中,即RIDRL,来解决作业车间调度问题。在这种方法中,我们手动选择五个可概括的状态特征作为车间环境的状态。采用18条调度规则作为智能体的动作空间,同时设计快速收敛的奖励函数。此外,我们使用了一种近端策略优化算法(PPO)来训练DRL代理,以最小化makespan为优化目标。在多个标准实例上的仿真实验表明,该方法对不同规模的问题都能得到竞争性的解。
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