Solving Markov decision processes for network-level post-hazard recovery via simulation optimization and rollout
For civil engineers and emergency managers, this provides a computationally efficient approach to near-optimal recovery planning for community water networks after earthquakes.
This paper tackles the combinatorial decision-making problem of post-hazard recovery for water networks under uncertainty, formulating it as a Markov decision process and solving it with rollout and Optimal Computing Budget Allocation (OCBA). The method achieves competitive performance using only 5-10% of the simulation budget required by rollout with total equal allocation.
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.