The lack of a comprehensive decision-making approach at the community level is an important problem that warrants immediate attention. Network-level decision-making algorithms need to solve large-scale optimization problems that pose computational challenges. The complexity of the optimization problems increases when various sources of uncertainty are considered. This research introduces a sequential discrete optimization approach, as a decision-making framework at the community level for recovery management. The proposed mathematical approach leverages approximate dynamic programming along with heuristics for the determination of recovery actions. Our methodology overcomes the curse of dimensionality and manages multi-state, large-scale infrastructure systems following disasters. We also provide computational results showing that our methodology not only incorporates recovery policies of responsible public and private entities within the community but also substantially enhances the performance of their underlying strategies with limited resources. The methodology can be implemented efficiently to identify near-optimal recovery decisions following a severe earthquake based on multiple objectives for an electrical power network of a testbed community coarsely modeled after Gilroy, California, United States. The proposed optimization method supports risk-informed community decision makers within chaotic post-hazard circumstances.

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 decisionmaking 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 nonmyopic 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.Saeed.Nozhati, Bruce.Ellingwood,

Following the occurrence of an extreme natural or man-made event, community recovery management should aim at providing optimal restoration policies for a community over a planning horizon. Calculating such optimal restoration polices in the presence of uncertainty poses significant challenges for community leaders. Stochastic scheduling for several interdependent infrastructure systems is a difficult control problem with huge decision spaces. The Markov decision process (MDP)-based optimization approach proposed in this study incorporates different sources of uncertainties to compute the restoration policies. The computation of optimal scheduling schemes using our method employs the rollout algorithm, which provides an effective computational tool for optimization problems dealing with real-world large-scale networks and communities. We apply the proposed methodology to a realistic community recovery problem, where different decision-making objectives are considered. Our approach accommodates current restoration strategies employed in recovery management. Our computational results indicate that the restoration policies calculated using our techniques significantly outperform the current recovery strategies. Finally, we study the applicability of our method to address different risk attitudes of policymakers, which include risk-neutral and risk-averse attitudes in the community recovery management. become complicated. The most important characteristics of a rational decision-making approach include:i. The agent must balance the reluctance for low immediate reward with the desire of high future rewards (also referred as "non-myopic agent" or look-ahead property); ii.The agent must consider different sources of uncertainties; iii. The agent must make decisions periodically to not only take advantage of information that becomes available when recovery actions are in progress but also to adapt to disturbances over the recovery process; iv.The agent must be able to handle a large decision-making space, which is typical for the problems at the community level. This decision-making space can cause an agent to suffer from decision fatigue; no matter how rational and high-minded an agent tries to be, one cannot make decision after decision without paying a cost [1]. v.The agent must consider different types of dependencies and interdependencies among networks, because a single decision can trigger cascading effects in multiple networks at the community level. vi.The agent must be able to handle multi-objective tasks, which are common in real-world domains. The interconnectedness among networks and probable conflicts among competing objectives complicate the decision-making procedure. vii.The agent must consider different constraints, such as time constraints, limited budget and repair crew, and current regional entities' policies. viii.External factors, like the available resources and the type of community and hazard, shape the risk attitude of the agent. The different risk behaviors must be considered.Community-level de...

We develop a method for autonomous management of multiple heterogeneous sensors mounted on unmanned aerial vehicles (UAVs) for multitarget tracking. The main contribution of the paper is incorporation of feedback received from intelligence assets (humans) on priorities assigned to specific targets. We formulate the problem as a partially observable Markov decision processes (POMDP) where information received from assets is captured as a penalty on the cost function. The resulting constrained optimization problem is solved using an augmented Lagrangian method. Information obtained from sensors and assets is fused centrally for guiding the UAVs to track these targets.

We investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain. We propose a closed-loop active sensing system to address this problem by exploiting three distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted waveform; and (3) motion model diversity by using a bank of parallel filters matched to different motion models. Specifically, at every radar scan, the waveform that yields the minimum trace of the one-step-ahead error covariance matrix is transmitted; the received signal goes through a matched-filter, and curve fitting is used to extract range and range-rate measurements that feed the LMIPDA-VSIMM algorithm for data association and filtering. Monte Carlo simulations demonstrate the effectiveness of the proposed system in an urban scenario contaminated by dense and uneven clutter, strong multipath, and limited line-of-sight.