Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet of Drones Performing Monitoring Missions

TL;DR

This paper extends the multiple traveling salesman problem to schedule drone fleets for monitoring tasks, proposing an ILP formulation and a scalable greedy heuristic with near-optimal performance.

Contribution

It introduces a novel extension of the M-TSP for drone scheduling and develops a scalable greedy algorithm with high near-optimal accuracy.

Findings

The greedy algorithm achieves 92.06% of the optimal solution on average.

The ILP formulation is effective for small problem instances.

The approach can scale to hundreds of drones and locations.

Abstract

In this paper we schedule the travel path of a set of drones across a graph where the nodes need to be visited multiple times at pre-defined points in time. This is an extension of the well-known multiple traveling salesman problem. The proposed formulation can be applied in several domains such as the monitoring of traffic flows in a transportation network, or the monitoring of remote locations to assist search and rescue missions. Aiming to find the optimal schedule, the problem is formulated as an Integer Linear Program (ILP). Given that the problem is highly combinatorial, the optimal solution scales only for small sized problems. Thus, a greedy algorithm is also proposed that uses a one-step look ahead heuristic search mechanism. In a detailed evaluation, it is observed that the greedy algorithm has near-optimal performance as it is on average at 92.06% of the optimal, while it can potentially scale up to settings with hundreds of drones and locations.