Agent-Based Control of Interaction Areas in Intralogistics: Concept, Implementation and Simulation

Felix Gehlhoff (), Niklas Jobs and Vincent Henkel
Additional contact information
Felix Gehlhoff: Institute of Automation Technology, Helmut Schmidt University/University of the Federal Armed Forces Hamburg, 22043 Hamburg, Germany
Niklas Jobs: Institute of Automation Technology, Helmut Schmidt University/University of the Federal Armed Forces Hamburg, 22043 Hamburg, Germany
Vincent Henkel: Institute of Automation Technology, Helmut Schmidt University/University of the Federal Armed Forces Hamburg, 22043 Hamburg, Germany

Logistics, 2025, vol. 9, issue 2, 1-33

Abstract: Background : Intralogistics systems face growing challenges from globalization, individualization, and shorter product life cycles, demanding flexible and responsive solutions beyond traditional centralized control. Decentralized, agent-based approaches offer potential advantages, especially for Automated Guided Vehicle (AGV) systems where managing collisions in interaction areas remains a critical issue. Methods : This study proposes two decentralized, agent-based control concepts for AGV systems in intralogistics. One uses a hierarchical model with an Intersection Manager to coordinate AGV agents, while the other employs a fully heterarchical system. For benchmarking, a First Come, First Served heuristic and a Mixed-Integer Linear Programming (MILP) method are also implemented. Simulations show both agent-based approaches effectively prevent collisions and uphold order prioritization and timing goals. While average delays are similar, the heterarchical system requires up to 2.7 times more communication. Priority-based control enhances timeliness for highpriority vehicles but can increase delays for lower-priority AGVs. The MILP method, though effective, is limited by impractical computation times. Results : The study confirms the viability of agent-based control for managing interaction areas in AGV systems, highlighting trade-offs between decentralization, efficiency, and communication. Conclusions : It offers a foundation for further research into hybrid models and real-world application of decentralized control strategies.

Keywords: agent-based control; automated guided vehicles; autonomous robots; collision avoidance; multi-agent systems (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2305-6290/9/2/52/pdf (application/pdf)
https://www.mdpi.com/2305-6290/9/2/52/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:9:y:2025:i:2:p:52-:d:1634448

Access Statistics for this article

Logistics is currently edited by Ms. Mavis Li

More articles in Logistics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().