Routing for unmanned aerial vehicles: touring dimensional sets

TL;DR

This paper extends the crossing postman problem to optimize drone routes visiting various geometric elements, introducing new mathematical models and a heuristic algorithm that efficiently solve medium-sized instances.

Contribution

It presents two novel mathematical programming formulations for drone routing with geometric neighborhoods and a heuristic method for larger problem instances.

Findings

Models solve medium-sized instances to optimality.

Heuristic algorithm provides high-quality solutions quickly.

Models are comparable to other neighborhood-based combinatorial problems.

Abstract

In this paper we deal with an extension of the crossing postman problem to design Hamiltonian routes that have to visit different shapes of dimensional elements (neighborhoods or polygonal chains) rather than edges. This problem models routes of drones that must visit a number of geographical elements to deliver some good or service and then move directly to the next target element using straight line displacements. We present two families of mathematical programming formulations. The first one is time-dependent and captures a number of actual characteristics of real applications at the price of using three indexes variables. The second one are not referenced to the stages of the route. We compare them on a testbed of randomly generated instances with different shapes of elements: second order cone representable (SOC) and polyhedral neighborhoods and polygonal chains. The computational result reported in this paper show that our models are useful and can solve to optimality medium size instances of sizes similar to other combinatorial problems with neighborhoods. To address larger instances we also present a heuristic algorithm that runs in two phases: clustering and VNS. This algorithm performs very well in quality of solutions provided and can be used to initialize the exact methods with promising initial solutions.