  Severe weather events such as floods, hurricanes, earthquakes, and large wind
or ice storms can cause extensive damage to electrical distribution networks,
requiring a multi-day restoration effort. Complicating the recovery process is
the lack of complete and accurate information regarding the extent and
locations of damages, at least during the initial part of the recovery process.
These factors make workforce planning challenging. In this paper, we adopt a
rolling horizon restoration framework whereby repairs are planned for
adjustable finite length restoration windows. Considering both repair times as
well as travel times, we show that the optimal scheduling problem with multiple
crews, each with their own time budget, can be recast in terms of a cost
constrained reward maximizing mTSP (traveling salesman problem) on doubly
weighted graphs, where the objective is to maximize the aggregate reward earned
during the upcoming restoration window, provided no crew violates its time
budget and certain electrical continuity constraints are met. We propose a
mixed integer linear programming (MILP) model for solving the above problem
which is validated on standard IEEE PES test feeder networks.
