## 1 Introduction
For executing the algorithm selection process, researchers make use of different techniques to select algorithms from several algorithms for solving optimization problems, such as integration or hyper-heuristics. Hyper-heuristics select between low-level heuristics for solving optimization problems [13]. They operate at a higher level of abstraction than traditional heuristics, which makes them more flexible and adaptable to different problem domains. Additionally, it is possible to use integration as part of an algorithm selection process. Therefore, a set of algorithms and performance metrics is defined, and an integration method calculates the overall performance of each algorithm based on metrics [14]. The difference between these ideas, in essence, is that the algorithm selection method refers to the overall process of selecting an algorithm for a given problem, which can involve a variety of different techniques and approaches. These techniques can be integration, metaheuristics, or hyper-heuristics, but machine learning also finds frequent application. Machine learning has received considerable attention in recent years due to its ability to learn patterns in data relating to a specified output. It extracts useful knowledge from the generated data throughout the search process and can support the detection of a problem class by evaluating incoming scheduling problems according to their individual character istics. Therefore, machine learning algorithms were implemented to select algorithms for several problem areas [15]. The motivation lies in the successful application of algorithm selection approaches in other domains. One outstanding example within the field of satisfiability problems (SAT) is the portfolio-based algorithm selection _SATzilla_[16]. Instead of traditionally choosing the best solver for a given class of instances, this decision is online on a per-instance basis. The approach takes a distribution of problem instances and a set of component solvers as input. It constructs a portfolio that optimizes a given objective function. Furthermore, [PERSON]. [17; 18] introduced algorithm selection approaches for two well-known production planning problems: the job-shop scheduling problem (JSSP) and the flexible job-shop scheduling problem (F)SSP). In [17], the authors proposed a CP model with several solvers for the FJSSP and showed that there was no clear winner over all tested instances. The same results were obtained in [18] by testing several state-of-the-art algorithms for the JSSP. They both implemented machine learning algorithms to select the best solver for specific instances of the individual scheduling problem class. This article aimed to develop an algorithm selector that is capable of finding the most suitable scheduling solution strategy with machine learning to determine the best schedule for a particular AGV problem instance. An automated algorithm selector was built from two state-of-the-art solvers for the JSSP and FJSSP, which were transferred to AGV scheduling. Several machine learning models were designed to decide which algorithm to run on an incoming instance. By selecting the algorithm for every given problem instance, a scheduling system can react dynamically to incoming scheduling problem types. There is no static algorithm presented. Instead, the instance is analyzed, and the suitable algorithm, based on the input features, is chosen. Furthermore, more than one overall problem type is taken into account. This enables a manufacturing system to be flexible in its approach to sending transport requests. These can already be assigned to an AGV, or the scheduler can allocate the orders to AGVs. The selection of an appropriate algorithm for individual scheduling scenarios enables a dynamic procedure of scheduling. To this end, the applied methodology to develop the algorithm selector is first presented in Section 2. The analysis phase unveils the selection of the algorithm base and the theoretical concept of the selector.