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Co-optimization of Short- and Long-term Decisions for the Transmission Grid’s Resilience to Flooding

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arXiv 2025年

作者： Shukla, Ashutosh Kutanoglu, Erhan Hasenbein, John Graduate Program in Operations Research and Industrial Engineering The University of Texas at Austin AustinTX United States

We present and analyze a three-stage stochastic optimization model that integrates output from a geoscience-based flood model with a power flow model for transmission grid resilience planning against flooding. The proposed model coordinates the decisions made across multiple stages of resilience planning and recommends an optimal allocation of the overall resilience investment budget across short- and long-term measures. While doing so, the model balances the cost of investment in both short- and long-term measures against the cost of load shed that results from unmitigated flooding forcing grid components go out-of-service. We also present a case study for the Texas Gulf Coast region to demonstrate how the proposed model can provide insights into various grid resilience questions. Specifically, we demonstrate that for a comprehensive yet reasonable range of economic values assigned to load loss, we should make significant investments in the permanent hardening of substations such that we achieve near-zero load shed. We also show that not accounting for short-term measures while making decisions about long-term measures can lead to significant overspending. Furthermore, we demonstrate that a technological development enabling to protect substations on short notice before imminent hurricanes could vastly influence and reduce the total investment budget that would otherwise be allocated for more expensive substation hardening. Lastly, we also show that for a wide range of values associated with the cost of mitigative long-term measures, the proportion allocated to such measures dominates the overall resilience spending. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

Scenario-based Optimization Models for Power Grid Resilience to Extreme Flooding Events

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arXiv 2023年

作者： Shukla, Ashutosh Kutanoglu, Erhan Hasenbein, John J. Graduate Program in Operations Research and Industrial Engineering The University of Texas at Austin Austin United States

We propose two scenario-based optimization models for power grid resilience decision making that integrate output from a hydrology model with a power flow model. The models are used to identify an optimal substation hardening strategy against potential flooding from storms for a given investment budget, which if implemented enhances the resilience of the power grid, minimizing the power demand that is shed. The same models can alternatively be used to determine the optimal budget that should be allocated for substation hardening when long-term forecasts of storm frequency and impact (specifically restoration times) are available. The two optimization models differ in terms of capturing risk attitude: one minimizes the average load shed for given scenario probabilities and the other minimizes the worst-case load shed without needing scenario probabilities. To demonstrate the efficacy of the proposed models, we further develop a case study for the Texas Gulf Coast using storm surge maps developed by the National Oceanic and Atmospheric Administration and a synthetic power grid for the state of Texas developed as part of an ARPA-E project. For a reasonable choice of parameters, we show that a scenario-based representation of uncertainty can offer a significant improvement in minimizing load shed as compared to using point estimates or average flood values. We further show that when the available investment budget is relatively high, solutions that minimize the worst-case load shed can offer several advantages as compared to solutions obtained from minimizing the average load shed. Lastly, we show that even for relatively low values of load loss and short post-hurricane power restoration times, it is optimal to make significant investments in substation hardening to deal with the storm surge considered in the NOAA flood scenarios. © 2023, CC BY.

关键词： Hurricanes

Joint optimization of logistics operations and reliability-based replacement policies in a geographically distributed service parts logistic system

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CIRP Annals 2025年

作者： Wang, Po-Han Djurdjanovic, Dragan Graduate Program on Operations Research and Industrial Engineering The University of Texas at Austin AustinTX United States Walker Department of Mechanical Engineering The University of Texas at Austin AustinTX United States

A joint location-inventory-maintenance model is proposed for a geographically distributed Service Parts Logistics problem. The model uses a reliability-based replacement strategy and is formulated as a quadratic Mixed Integer Linear program which jointly optimizes logistic and preventive part replacement decisions. The proposed solution method employs piecewise linearization to provide a level of tractability and scalability to the method. The newly proposed joint location-inventory-maintenance model is evaluated and benchmarked against traditional local optimum-based replacement approaches. It consistently outperformed the benchmark models, with the efficiency of cost effect improvements determined by the relative prevalence of logistics factors versus asset management costs. © 2025

关键词： Piecewise linear techniques

Distributionally Robust Observable Strategic Queues

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arXiv 2022年

作者： Wang, Yijie Prasad, Madhushini Narayana Hanasusanto, Grani A. Hasenbein, John J. Graduate Program in Operations Research and Industrial Engineering The University of Texas Austin United States

This paper presents an extension of Naor's analysis on the join-or-balk problem in observable M/M/1 queues. While all other Markovian assumptions still hold, we explore this problem assuming uncertain arrival rates under the distributionally robust settings. We first study the problem with the classical moment ambiguity set, where the support, mean, and mean-absolute deviation of the underlying distribution are known. Next, we extend the model to the data-driven setting, where decision makers only have access to a finite set of samples. We develop three optimal joining threshold strategies from the perspective of an individual customer, a social optimizer, and a revenue maximizer, such that their respective worst-case expected benefit rates are maximized. Finally, we compare our findings with Naor's original results and the traditional sample average approximation scheme. © 2022, CC BY.

关键词： Decision making

Deep Learning-Based Residual Useful Lifetime Prediction for Assets With Uncertain Failure Modes

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Journal of Computing and Information Science in engineering 2025年第4期25卷 041002页

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University 915 Partners Way Raleigh NC 27607 Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University 915 Partners Way Raleigh NC 27607

Industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing prognostic models for systems with multiple failure modes face several challenges in real-world applications, including overlapping degradation signals from multiple components, the presence of unlabeled historical data, and the similarity of signals across different failure modes. To tackle these issues, this research introduces two prognostic models that integrate the mixture (log)-location-scale distribution with deep learning. This integration facilitates the modeling of overlapping degradation signals, eliminates the need for explicit failure mode identification, and utilizes deep learning to capture complex nonlinear relationships between degradation signals and residual useful lifetimes. Numerical studies validate the superior performance of these proposed models compared to existing methods.

关键词： Failure mechanisms mixture (log)-location-scale distribution Failure Weibull distribution multiple failure modes Signals Service life (Equipment) prognostics data analytics Deep learning machine learning for engineering applications engineering systems and industry applications industrial Internet of things big data and analytics

Differentially Private Log-Location-Scale Regression Using Functional Mechanism

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arXiv 2024年

作者： Sheng, Jiewen Fang, Xiaolei Graduate Program in Operations Research North Carolina State University United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University United States

This article introduces differentially private log-location-scale (DP-LLS) regression models, which incorporate differential privacy into LLS regression through the functional mechanism. The proposed models are established by injecting noise into the log-likelihood function of LLS regression for perturbed parameter estimation. We will derive the sensitivities utilized to determine the magnitude of the injected noise and prove that the proposed DP-LLS models satisfy ϵ-differential privacy. In addition, we will conduct simulations and case studies to evaluate the performance of the proposed models. The findings suggest that predictor dimension, training sample size, and privacy budget are three key factors impacting the performance of the proposed DP-LLS regression models. Moreover, the results indicate that a sufficiently large training dataset is needed to simultaneously ensure decent performance of the proposed models and achieve a satisfactory level of privacy protection. © 2024, CC BY.

关键词： Budget control

Virtual Wearable Sensor Data Generation with Generative Adversarial Networks

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Virtual Wearable Sensor Data Generation with Generative Adve...

2023 Winter Simulation Conference, WSC 2023

作者： Huang, Yining Wan, Hong Chen, Xi North Carolina State University Operations Research Graduate Program 915 Partners Way RaleighNC27606 United States North Carolina State University Department of Industrial and Systems Engineering 915 Partners Way RaleighNC27606 United States Virginia Tech Grado Department of Industrial and Systems Engineering 1145 Perry Street BlacksburgVA24061 United States

ISBN: (纸本)9798350369663

This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offeringaninnovativealternativetotraditionalmetamodel-basedsimulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs' capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems. © 2023 IEEE.

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Virtual Wearable Sensor Data Generation with Generative Adversarial Networks 23

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Virtual Wearable Sensor Data Generation with Generative Adve...

Proceedings of the Winter Simulation Conference

作者： Yining Huang Hong Wan Xi Chen Operations Research Graduate Program North Carolina State University Raleigh USA Department of Industrial and Systems Engineering North Carolina State University Raleigh USA Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg USA

ISBN: (纸本)9798350369663

This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offering an innovative alternative to traditional metamodel-based simulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs' capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems.

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Blockchain: A Review from the Perspective of operations researchers 22

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Blockchain: A Review from the Perspective of Operations Rese...

Proceedings of the Winter Simulation Conference

作者： Hong Wan Kejun Li Yining Huang Department of Industrial and Systems Engineering North Carolina State University Raleigh NC USA Operations Research Graduate Program North Carolina State University Raleigh NC USA

Blockchain is a distributed append-only digital ledger. The technology has caught much attention since the emergence of cryptocurrency, and there is an increasing number of blockchain applications in various businesses. The concept, however, is still novel to many members of the simulation and operations research community. In this tutorial, we introduce the blockchain technology and review its frontier related research. There are exciting opportunities for researchers in simulation, system analysis, and data science.

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