Solving Markov decision processes for network-level post-hazard recovery via simulation optimization and rollout

TL;DR

This paper develops a simulation optimization approach using rollout and OCBA algorithms to efficiently solve large-scale Markov decision processes for water network recovery after earthquakes, improving decision-making under uncertainty.

Contribution

It introduces a novel combination of rollout and OCBA algorithms for stochastic optimization in community recovery, enabling effective large-scale MDP solutions with limited simulation budgets.

Findings

Rollout with OCBA outperforms traditional methods at low simulation budgets.

The approach effectively models damage and recovery in water networks post-earthquake.

Simulation results demonstrate competitive performance with significantly fewer resources.

Abstract

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.