Reinforcement Learning, Optimization and Scheduling in Industry 5.0: Overview and Case studies

Sinigaglia, Alberto and De Monte, Riccardo and Stöckermann, Patrick and Immordino, Alessandro and Celenk, Ilayda and van Jaarsveld, Willem and Adan, Ivo and Weijers, Eric and Rosman, Cas and de Jong, Bibi and Schelthoff, Kai and Hoekstra, Bram and Leja, Laura and Susto, Gian Antonio and Teikmanis, Oskars and Freivalds, Karlis (2026) Reinforcement Learning, Optimization and Scheduling in Industry 5.0: Overview and Case studies. INFOCOMMUNICATIONS JOURNAL, 18 (Specia). pp. 104-118. ISSN 2061-2079

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Abstract

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.

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