Generalising the Team Orienteering problem through Multi-constraining and Multi-objectives

Among all combinatorial optimization challenges, the class of routing represents a well-known set of problems whose optimized solutions can significantly improve overall solution quality. Within this set, the Orienteering Problem presents a complex challenge: finding a set of Hamiltonian paths from a selected subset of objective points within a given time frame to maximize a global score. In this paper, the Multi-constrained Team Orienteering Problem is addressed by incorporating time windows and constraints on the maximum number of categories. The inclusion of an additional objective to balance route sizes is also explored. The problem is solved by adapting the NSGA-II algorithm, incorporating an integer-based solution representation and a set of transformation operators to achieve high-quality, densely populated Pareto fronts with reduced computation times compared to complete solvers.