New mathematical formulations for the generalized teamorienteering problems

Team Orienteering Problem (TOP) is an optimization problem in which an optimal tour is searched for a team of travelers, previously specified number (m), under the constraint of a maximum travel time and the objective function of the problem is to maximize the profit which is collected from the customers. In the problem, every customer is not necessarily visited. The generalized version of Selective Traveling Salesman Problem in which the customers are grouped as clusters is studied in the literature. New mathematical formulations for the Generalized Team Orienteering Problem (GTOP) are proposed in this thesis, since there is a gap in the literature about the generalization of the TOP. Two models are proposed with the additional sequence-based decision variables for customers/clusters and two models are proposed with the additional sequence-based decision variables for arcs between customers/clusters. Performances of the four models are tested on the test problems. 9216 problems are solved, and the proposed models are able to find the optimal solutions for the %84 of the problems. For the small and medium sized problems edge-based models, for the big sized problems node-based models find more optimal solutions. In overall, edge-based models are observed to be able to find more solutions than the node-based problems.