The Information-Collecting Vehicle Routing Problem: Stochastic Optimization for Emergency Storm Response

We address the problem of mitigating damage to a power grid following a storm by managing a vehicle that has to be routed while simultaneously performing two tasks: learning about damage from the grid (which requires direct observation) and repairing damage that it observes. The learning process is assisted by calls from customers notifying the utility that they have lost power (``lights-out calls''). However, when a tree falls and damages a line, it triggers the first upstream circuit breaker, which results in power outages for everyone on the grid below the circuit breaker. We present a dynamic routing model that captures observable state variables such as the location of the truck and the state of the grid on segments the truck has visited, and beliefs about outages on segments that have not been visited. Trucks are routed over a physical transportation network, but the pattern of outages is governed by the structure of the power grid. We introduce a form of Monte Carlo tree search based on information relaxation that we call {\it optimistic MCTS} which improves its application to problems with larger action spaces. We show that the method significantly outperforms standard escalation heuristics used in industry.}