## 1 Introduction  
### Motivation and Problem Statement  
Automation and the digital transformation of workflows are becoming an increasingly important factor in today's work environments. The physical and digital integration of machines, material, data, and workforce is being pushed forth under the keyword Industry 4.0. The reduction in costs and increasing flexibility, speed, and quality are the leading promises. In order to deliver on these promises, Industry 4.0 applications involve interconnected devices, cloud services, advanced automation, and Artificial Intelligence (AI) technologies [1, 2]. Thereby, any relevant detail of the involved physical entities in the environment also has a Digital Twin (DT) representation. Changes to the physical object are tracked and synchronised with the DT. Changes on the DT will be considered during the processing of the physical object [3]. [PERSON] et al. [4] describe a _functional digital twin_ that actually represents a simulation, which is initialised from the most recent state of the environment tracked in the DT. It is deployed to explore system behaviours and evaluate the effects of measures and to predict and control the connected risks. The availability of the actual state of the production environment in a machine readable form enables computer systems to support or take over responsibility in decision-making and robotic systems to execute physical tasks, especially in form of autonomous material handling. An important role in the implementation of efficient Industry 4.0 production environments comes to the automation of the intralogistics. The digital integration enables Automated Material Handling Systems (AMHS), e.g., via fleets of Autonomous Mobile Robots (AMR), to provide for the manufacturing processes effectively, flexibly and with low coordination overhead [5; 6; 7; 8]. Through the application of Artificial Intelligence methods, the system can automatically adapt to emerging situations and optimise its behaviour as required. Already today, those industries whose identity mostly revolves around logistics are especially able to exploit the massive scaling effects of autonomous robotic fleets (e.g., [9; 10]). Yet, automated material handling is still an active research field. Common research questions are the evaluation and benchmarking of different factory layouts, numbers of vehicles deployed, dispatching strategies or related factors [11], often before the background of a specific case study. [PERSON] et al. [12] employ a simulation to evaluate the performance of three configurations regarding the number of pickers in the supermarket (i.e., a central storage area from which material is distributed) and behavioural adaptations for the Automated Guided Vehicle (AGV) during operation. They calculate a benefit in terms of different metrics for those configurations. [PERSON] et al. [13] report an evaluation with a discrete-event simulation (DES) for trading off numbers of vehicles, layouts, idle/charging policies with performance indicators. Other recent approaches incorporate machine learning algorithms, e.g., for the estimation of travel times to achieve better-informed scheduling decisions [14], or load balancing within the factory through the adoption of entropy-based calculations to improve overall system performance [15]. [PERSON] et al. [16] propose to use a genetic algorithm to factor various heuristics into a meta-heuristic, here, to take into account stochastic variations in transport time and to improve compliance with required windows of delivery time. Instead of a practical evaluation of the methods, e.g., in a simulation, they compare the methods based on fitness functions directly calculated from the produced task assignments. Similar research questions are actively being discussed in the field of on-demand mobility, taxi dispatching, or ride-hailing systems. Here, the layout of the navigation graph usually cannot be adjusted, but the same mechanisms apply, e.g., for order matching, vehicle dispatching or idle vehicle repositioning [17; 18].