  We present the Limited-range Online Routing Problem (LORP), which involves a
team of Unmanned Aerial Vehicles (UAVs) with limited communication range that
must autonomously coordinate to service task requests. We first show a general
approach to cast this dynamic problem as a sequence of decentralized task
allocation problems. Then we present two solutions both based on modeling the
allocation task as a Markov Random Field to subsequently assess decisions by
means of the decentralized Max-Sum algorithm. Our first solution assumes
independence between requests, whereas our second solution also considers the
UAVs' workloads. A thorough empirical evaluation shows that our workload-based
solution consistently outperforms current state-of-the-art methods in a wide
range of scenarios, lowering the average service time up to 16%. In the
best-case scenario there is no gap between our decentralized solution and
centralized techniques. In the worst-case scenario we manage to reduce by 25%
the gap between current decentralized and centralized techniques. Thus, our
solution becomes the method of choice for our problem.
