## 1 Introduction  
With the introduction of Industry 4.0, production systems have become increasingly complex within the last few years [1]. This is, on one hand, due to the growing size, but it is also due to the extended automation influences in manufacturing environments. As a result, the demands on intralogistics in terms of flexibility and internal control are increasing. One major development within the technology of material handling is the evolution of autonomous mobile robots (AMRs), including AGVs [2]. AGVs are wheel-based and unmanned vehicles that transport small or large unit loads along the floor of a facility. These load carriers provide the internal and external transport of materials [3]. Two of the main enabling software technologies behind AGVs are scheduling and routing. Thereby, dynamic AGV scheduling models have the potential to minimize delays and reduce production costs by facilitating a minimal production makespan [4]. Scheduling, in general, is a decision-making process that deals with the allocation of resources to tasks over given time periods, while the goal is to optimize one or more objectives [5]. Thereby, resources and tasks can take many different forms, and the objectives themselves may range from a minimization of the completion time of the last task to the minimization of overall delay, depending on the use case. Scheduling can be either static or dynamic; in static scheduling, all task information is stable and obtained in advance, while dynamic scheduling considers uncertainties on the shop floor [6]. The majority of scheduling problems are NP-hard problems, which are more difficult to solve than others. This is because the computation time required for solving a problem increases faster as the size of the problem grows [7]. There are a wide variety of approaches for solving scheduling problems. However, they differ in their solution quality. Traditional scheduling approaches include three major areas: heuristic approaches, such as dispatching rules; metaheuristics, such as simulated annealing, tabu search, local search, and genetic algorithms; and exact methods, such as mathematical programming (MP), mixed-integer programming (MIP), linear programming (LP), integer linear programming (ILP), and mixed integer linear programming (MILP) [8; 9]. Another emerging technology for optimizing scheduling problems is constraint programming (CP). The dynamic selection of the appropriate algorithm for a specific scheduling problem is a new challenge, especially in the case of changing framework conditions. Considering the new complexity arising within production systems, the dynamic selection of the most appropriate algorithm for a specific scheduling problem can be beneficial. Depending on the scheduling problem, some algorithms might be more efficient than others, especially when considering the specific instances within a scheduling problem. Algorithm selection has been part of research for a long time. The algorithm selection problem was introduced in 1976 [10]. Three major characteristics help to categorize a model for algorithm selection: the problem space, algorithm space, and performance space. Algorithm selection has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms [11]. This is because, in some scenarios, a new approach will improve the current state of the art but only for some problems. Selecting the most suitable algorithm for a particular problem aims to mitigating these problems and has the potential to significantly increase performance in practice [12]. Researchers use the benefits of algorithm selection to explore the details of data and to make use of data characteristics in order to choose the most promising algorithm to save computational time and increase performance.