A method for assigning a plurality of vehicles to a plurality of vehicular tasks, the method comprising: Obtaining information on the plurality of vehicular tasks; Obtaining information on the plurality of vehicles; Providing the information on the plurality of vehicular tasks and the information on the plurality of vehicles as input to a machine-learning model, wherein the machine-learning model is suitable for evaluating a plurality of assignments between the plurality of vehicles and the plurality of vehicular tasks, wherein an output of the machine-learning model indicates a favorability of the plurality of assignments between the plurality of vehicles and the plurality of vehicular tasks, wherein the machine-learning model is trained the aim of improving a result of an objective function, wherein the objective function is suitable for evaluating a favorability of a plurality of assignments between the plurality of vehicles and the plurality of vehicular tasks, and Assigning the plurality of vehicles to the plurality of vehicular tasks based on the output of the machine-learning model, wherein the information on the plurality of vehicular tasks and the information on the plurality of vehicles is provided as a bipartite graph to the machine-learning model, the graph comprising the plurality of vehicular tasks, and the plurality of vehicles as nodes and a plurality of potential assignments between vehicles and vehicular tasks as edges of the graph, wherein the output of the machine-learning model indicates the favorability of the plurality of assignments using the edges of the graph, wherein the objective function is to find the maximum cardinality matching which contains the largest quantity of edges that globally maximizes the range of vehicles and minimizes the selected covered distances among vehicles. Obtaining information on the plurality of vehicular tasks; Obtaining information on the plurality of vehicles; Providing the information on the plurality of vehicular tasks and the information on the plurality of vehicles as input to a machine-learning model, wherein the machine-learning model is suitable for evaluating a plurality of assignments between the plurality of vehicles and the plurality of vehicular tasks, wherein an output of the machine-learning model indicates a favorability of the plurality of assignments between the plurality of vehicles and the plurality of vehicular tasks, wherein the machine-learning model is trained the aim of improving a result of an objective function, wherein the objective function is suitable for evaluating a favorability of a plurality of assignments between the plurality of vehicles and the plurality of vehicular tasks, and Assigning the plurality of vehicles to the plurality of vehicular tasks based on the output of the machine-learning model, wherein the information on the plurality of vehicular tasks and the information on the plurality of vehicles is provided as a bipartite graph to the machine-learning model, the graph comprising the plurality of vehicular tasks, and the plurality of vehicles as nodes and a plurality of potential assignments between vehicles and vehicular tasks as edges of the graph, wherein the output of the machine-learning model indicates the favorability of the plurality of assignments using the edges of the graph, wherein the objective function is to find the maximum cardinality matching which contains the largest quantity of edges that globally maximizes the range of vehicles and minimizes the selected covered distances among vehicles. <EOS>