Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling

arXiv:2211.10936v3 Announce Type: replace
Abstract: Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modelling partial solutions at each construction step. This paper proposes a novel DRL-guided improvement heuristic for solving JSSP, where graph representation is employed to encode complete solutions. We design a Graph Neural-Network-based representation scheme, consisting of two modules to …

abstract arxiv construction cs.ai cs.lg focus graph graph representation heuristics improvement job modelling paper performance reinforcement reinforcement learning representation scheduling solutions solve studies type