Robust multi-objective asset routing in a dynamic and uncertain environment

Routing in uncertain environments is challenging as it involves a number of contextual elements, such as different environmental conditions (forecast realizations with varying spatial and temporal uncertainty), multiple objectives, changes in mission goals while en route (e.g., training requirements, popup threats, humanitarian aid), and asset status. The United States Navy spends an estimated $35 billion per year on fuel. It is estimated that optimized weather routing can save upwards of $350 million per year solely from fuel savings. In this paper, we focus on robust planning under uncertainty by exploiting weather forecast realization information using TMPLAR, a Tool for Multi-objective PLanning and Asset Routing in the context of 3D and 4D navigation. Our overall approach includes computing R-best shortest paths for each weather forecast realization via Murty's search space partitioning strategy, and treat the mean, variance, and signal-to-noise ratio (SNR) of the paths as part of the cost function by considering all forecast realizations. We pick the robust path that minimizes the root mean square (RMS) value (or) maximizes the SNR, given the possible forecast realizations. We demonstrate their utility via application to a real-world shipping tragedy using weather forecast realizations available prior to the event.