Hubl, MarvinMichael, JudithPfeifferJérômeWortmann, Andreas2022-06-302022-06-302022https://dl.gi.de/handle/20.500.12116/38793Modeling of relations in a domain is a fundamental basis for solving domain problems. However, even well-formulated mathematical models do not always allow for satisfactory solutions. Here, methods from Artificial Intelligence bring value for solutions based on the formal models, e.\,g. by meta-heuristics. Furthermore, variables in a mathematical model may require manifestations although exact values are not known or measured. Machine-learning-based methods can enhance the appropriateness for the variable manifestation. We study upon these issues at the example of a process coordination problem in agricultural crop production. We analyze how methods of Artificial Intelligence can enhance processual efficiency and resilience. Therefore, two domain objectives are formalized: (i) maximization of machine utilization; (ii) maximization of aggregated area output. We identify and discuss the contribution of Artificial Intelligence for solving the mathematically formalized problem appropriately.enMathematical ModelingOptimization ProblemProcess CoordinationResource AllocationAgricultural EngineeringText/Conference Paper10.18420/modellierung2022ws-003