A biased-randomised iterated local search for the team orienteering arc routing problem allowing different origin and destination

In this paper, we consider the hybridisation of the team orienteering problem and the arc routing problem, the so-called team orienteering arc routing problem (TOARP). This problem has recently raised interest among researchers and practitioners as it can model new routing problems involving unmanned aerial vehicles or other types of electric vehicles. In the TOARP, a fixed fleet of vehicles, initially located at a depot, has to collect as much reward as possible from visiting a set of arcs, while they must visit a set of required arcs. At the same time, they must return to the depot on or before a given deadline (which can be time-based or distance-based). In this paper, we explore an extension of the TOARP in which the origin depot and the destination depot may be different nodes in a network. To solve this version of the problem, we propose a novel biased-randomised iterated local search algorithm. Computational results show the capability, efficiency and robustness of our approach, which provides competitive solutions to the TOARP in short computing times and outperforms some of the best-known solution approaches in the literature.