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Prethodno priopćenje

https://doi.org/10.31803/tg-20240420135237

Digital Twin and Simulation Analyses for Process Optimization of an Automated Guided Vehicle System

Lothar Schulze ; Leibniz Universität Hannover, Callinstr. 36, 30167 Hannover, Germany *
Li Li ; Technische Hochschule Ostwestfalen-Lippe, Campusallee 12, 32657 Lemgo, Germany

* Autor za dopisivanje.


Puni tekst: engleski pdf 604 Kb

str. 282-288

preuzimanja: 40

citiraj


Sažetak

Development in industrial companies aims to digitize processes, ensure traceability and advance automation. Systems with automated guided vehicles (AGVs) can help meet these objectives. A core element of these systems is the higher-level system controller. When an AGV system is operated, some dynamic aspects appear. It is then to identify, that the previous specified rules for scheduling and routing of vehicles are not always proper and practical, which may result in waiting time of AGVs. That means the static performance requirements and the specifications based on them often lead to suboptimal results. A methodical problem-solving approach is to develop a digital twin as a reflection of reality and to carry out strategy analyses using simulation. For this purpose, a digital twin of the concerned AGV system with five vehicles and its technological environment is created using software of plant simulation. Various simulation scenarios are developed to simulate material flows by using different routing and scheduling rules and strategies. Drawing from the insights gained through the digital twin and simulation analyses, this study identifies novel scheduling and routing rules for the AGV system. These rules improve the overall efficiency and effectiveness of the system operations by easing traffic congestion, reducing transit time, and minimizing production downtime.

Ključne riječi

Automated Guided Vehicle (AGV); digital twin; material flow optimization; routing and scheduling; simulation analysis

Hrčak ID:

316913

URI

https://hrcak.srce.hr/316913

Datum izdavanja:

31.5.2024.

Posjeta: 77 *