Alberto Sinigaglia, Riccardo De Monte, Patrick Stöckermann, Alessandro Immordino, Ilayda Celenk, Willem van Jaarsveld, Ivo Adan, Eric Weijers, Cas Rosman, Bibi de Jong, Kai Schelthoff, Bram Hoekstra, Laura Leja, Gian Antonio Susto, Oskars Teikmanis, and Karlis Freivalds
Reinforcement Learning, Optimization and Scheduling in Industry 5.0: Overview and Case studies
This chapter reviews recent advances in the application of Deep Reinforcement Learning (DRL) to industrial scheduling and supply chain optimization. We first introduce foundational RL concepts and leading DRL algorithms, as well as various other machine learning algorithms. A central component of the chapter are various case studies, where DRL and other methods are applied to company use cases. The chapter concludes with perspectives on scalability, deployment, and research opportunities within Industry 4.0 and 5.0 contexts.
Reference:
DOI: 10.36244/ICJ.2026.5.4
Please cite this paper the following way:
Alberto Sinigaglia, Riccardo De Monte, Patrick Stöckermann, Alessandro Immordino, Ilayda Celenk, Willem van Jaarsveld, Ivo Adan, Eric Weijers, Cas Rosman, Bibi de Jong, Kai Schelthoff, Bram Hoekstra, Laura Leja, Gian Antonio Susto, Oskars Teikmanis, and Karlis Freivalds "Reinforcement Learning, Optimization and Scheduling in Industry 5.0: Overview and Case studies", Infocommunications Journal, Industrial AI for Industry 5.0, 2026, pp. 104-118., https://doi.org/10.36244/ICJ.2026.5.4