An approximate dynamic programming approach to food security of communities following hazards

Food security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake.Comment: As opposed to the preemptive scheduling problem, which was addressed in multiple works by us, we deal with a non-preemptive stochastic scheduling problem in this work. Submitted to 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26-30, 201

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

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.Comment: Submitted to Simulation Optimization for Cyber Physical Energy Systems (Special Session) in 14th IEEE International Conference on Automation Science and Engineerin

Optimal stochastic scheduling of restoration of infrastructure systems from hazards: an approximate dynamic programming approach

2019 Summer.Includes bibliographical references.This dissertation introduces approximate dynamic programming (ADP) techniques to identify near-optimal recovery strategies following extreme natural hazards. The proposed techniques are intended to support policymakers, community stakeholders, and public or private entities to manage the restoration of critical infrastructure of a community following disasters. The computation of optimal scheduling schemes in this study employs the rollout algorithm, which provides an effective computational tool for optimization problems dealing with real-world large-scale networks and communities. The Markov decision process (MDP)-based optimization approach incorporates different sources of uncertainties to compute the restoration policies. The fusion of the proposed rollout method with metaheuristic algorithms and optimal learning techniques to overcome the computational intractability of large-scale, multi-state communities is probed in detail. Different risk attitudes of policymakers, which include risk-neutral and riskaverse attitudes in community recovery management, are taken into account. The context for the proposed framework is provided by objectives related to minimizing foodinsecurity issues and impacts within a small community in California following an extreme earthquake. Probabilistic food security metrics, including food availability, accessibility, and affordability, are defined and quantified to provide risk-informed decision support to policymakers in the aftermath of an extreme natural hazard. The proposed ADP-based approach then is applied to identify practical policy interventions to hasten the recovery of food systems and reduce the adverse impacts of food insecurity on a community. All proposed methods in this study are applied on a testbed community modeled after Gilroy, California, United States, which is impacted by earthquakes on the San Andreas Fault. Different infrastructure systems, along with their spatial distributions, are modeled as part of the evaluation of the restoration of food security within that community. The methods introduced are completely independent of the initial condition of a community following disasters and type of community (network) simulation. They treat the built environment like a black box, which means the simulation and consideration of any arbitrary network and/or sector of a community do not affect the applicability and quality of the framework. Therefore, the proposed methodologies are believed to be adaptable to any infrastructure systems, hazards, and policymakers' preferences

Krill herd algorithm-based neural network in structural seismic reliability evaluation

In this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Algorithm (GA), and the back propagation neural network model. The comparison of results has been carried out in the training and test phases. It has been revealed that the artificial neural network optimized with the krill herd algorithm supersedes the afore-mentioned models in potential, flexibility, and precision