An Evidence-Based Systematic Literature Review of Deep Reinforcement Learning for Manufacturing Scheduling

Abstract

Deep reinforcement learning (DRL) has become an important approach for manufacturing scheduling because it supports sequential decision-making under complex and changing production conditions. However, existing reviews primarily organize the literature by scheduling problem or learning method, providing less explicit support for tracing how manufacturing context, Markov Decision Process (MDP) formulation, scheduler architecture, and evaluation choices interact across heterogeneous studies. This study presents an evidence-based systematic literature review of DRL for manufacturing scheduling using a structured methodology for corpus construction, configuration-level coding, evidence traceability, study-quality assessment, and cross-study synthesis. The validated corpus comprises 52 primary studies and 54 independently coded DRL configurations. The evidence is synthesized across manufacturing scheduling characteristics, MDP design, DRL scheduler design, hybrid optimization, and empirical evaluation. The results show that scheduler design is context-dependent and architecturally diverse: manufacturing requirements are associated with differences in state, action, and reward formulation, while DRL schedulers combine different learning algorithms, representation architectures, control structures, and complementary optimization mechanisms. The evidence does not establish universal superiority for individual representations, algorithms, or hybrid architectures because reported outcomes remain strongly conditioned by problem formulation and experimental design. Evaluation evidence further highlights limited generalization, uneven statistical and component-level validation, and a continuing gap between benchmark or simulation studies and live industrial deployment. By linking study-, configuration-, and evidence-level information, this review provides a traceable basis for interpreting methodological relationships, identifying research gaps, and guiding the development and evaluation of DRL-based manufacturing scheduling systems.