A Framework for Applying Reinforcement Learning to Deadlock Handling in Intralogistics

Abstract

Intralogistics systems, while complex, are crucial for a range of industries. One of their challenges is deadlock situations that can disrupt operations and decrease efficiency. This paper presents a four-stage framework for applying reinforcement learning algorithms to manage deadlocks in such systems. The stages include Problem Formulation, Model Selection, Algorithm Selection, and System Deployment. We carefully identify the problem, select an appropriate model to represent the system, choose a suitable reinforcement learning algorithm, and finally deploy the solution. Our approach provides a structured method to tackle deadlocks, improving system resilience and responsiveness. This comprehensive guide can serve researchers and practitioners alike, offering a new avenue for enhancing intralogistics performance. Future research can explore the framework’s effectiveness and applicability across different systems.

Authors 4

1. Marcel Müller

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Institute of Logistics and Material Handling Systems, Otto von Guericke University Magdeburg

2. Lorena Silvana Reyes-Rubiano Aachen research fellow Chair of Data and Business Analytics

   RWTH Aachen University

   Affiliation as printed

   research fellow, Chair of Data and Business Analytics, RWTH Aachen University, Germany

3. Tobias Reggelin

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   research manager, Institute of Logistics and Material Handling Systems, Otto von Guericke University Magdeburg

4. Hartmut Zadek

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Institute of Logistics and Material Handling Systems, Otto von Guericke University Magdeburg