  Computation of optimal recovery decisions for community resilience assurance
post-hazard is a combinatorial decision-making problem under uncertainty. It
involves solving a large-scale optimization problem, which is significantly
aggravated by the introduction of uncertainty. In this paper, we draw upon
established tools from multiple research communities to provide an effective
solution to this challenging problem. We provide a stochastic model of damage
to the water network (WN) within a testbed community following a severe
earthquake and compute near-optimal recovery actions for restoration of the
water network. We formulate this stochastic decision-making problem as a Markov
Decision Process (MDP), and solve it using a popular class of heuristic
algorithms known as rollout. A simulation-based representation of MDPs is
utilized in conjunction with rollout and the Optimal Computing Budget
Allocation (OCBA) algorithm to address the resulting stochastic simulation
optimization problem. Our method employs non-myopic planning with efficient use
of simulation budget. We show, through simulation results, that rollout fused
with OCBA performs competitively with respect to rollout with total equal
allocation (TEA) at a meagre simulation budget of 5-10% of rollout with TEA,
which is a crucial step towards addressing large-scale community recovery
problems following natural disasters.
