  Route planning also known as pathfinding is one of the key elements in
logistics, mobile robotics and other applications, where engineers face many
conflicting objectives. However, most of the current route planning algorithms
consider only up to three objectives. In this paper, we propose a scalable
many-objective benchmark problem covering most of the important features for
routing applications based on real-world data. We define five objective
functions representing distance, traveling time, delays caused by accidents,
and two route specific features such as curvature and elevation. We analyse
several different instances for this test problem and provide their true
Pareto-front to analyse the problem difficulties. We apply three well-known
evolutionary multi-objective algorithms. Since this test benchmark can be
easily transferred to real-world routing problems, we construct a routing
problem from OpenStreetMap data. We evaluate the three optimisation algorithms
and observe that we are able to provide promising results for such a real-world
application. The proposed benchmark represents a scalable many-objective route
planning optimisation problem enabling researchers and engineers to evaluate
their many-objective approaches.
