1 research outputs found
The Information-Collecting Vehicle Routing Problem: Stochastic Optimization for Emergency Storm Response
Utilities face the challenge of responding to power outages due to storms and
ice damage, but most power grids are not equipped with sensors to pinpoint the
precise location of the faults causing the outage. Instead, utilities have to
depend primarily on phone calls (trouble calls) from customers who have lost
power to guide the dispatching of utility trucks. In this paper, we develop a
policy that routes a utility truck to restore outages in the power grid as
quickly as possible, using phone calls to create beliefs about outages, but
also using utility trucks as a mechanism for collecting additional information.
This means that routing decisions change not only the physical state of the
truck (as it moves from one location to another) and the grid (as the truck
performs repairs), but also our belief about the network, creating the first
stochastic vehicle routing problem that explicitly models information
collection and belief modeling. We address the problem of managing a single
utility truck, which we start by formulating as a sequential stochastic
optimization model which captures our belief about the state of the grid. We
propose a stochastic lookahead policy, and use Monte Carlo tree search (MCTS)
to produce a practical policy that is asymptotically optimal. Simulation
results show that the developed policy restores the power grid much faster
compared to standard industry heuristics