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Airline OR Innovations Soar During COVID-19 Recovery

Operations Research Forum 2022年第1期3卷 14页

作者： Garrow, Laurie A. Lurkin, Virginie Marla, Lavanya School of Civil and Environmental Engineering Georgia Institute of Technology Atlanta GA United States HEC Faculty of Business and Economics University of Lausanne Lausanne Switzerland Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL United States

The Airline Group of the International Federation of Operational Research Societies (AGIFORS) held four conferences during May to July 2021 that focused on how COVID-19 was impacting and reshaping the airline industry. Dozens of airline representatives from around the world spoke about how fundamental changes in passenger demand and booking patterns are reshaping the airline industry and driving innovation and research needs. Customers are booking much closer to departure and are canceling or exchanging their tickets more frequently than before COVID-19. Volatility in demand has increased as travel restrictions change and borders reopen. Consequently, greater uncertainty in demand forecasts used as inputs to optimization algorithms is motivating the need for new approaches. Revenue management and scheduling departments are innovating how they predict market sizes and exploring ways to use new data sources or historic bookings in forecasting models. Scheduling and operations departments are making many more flight-cancellation and equipment-swap decisions one to three days from flight departure, which is changing the role of recovery planning. New urgency exists within crew to design duties and rosters that are robust to ever-evolving schedules. Across functional areas, the increasing emphasis is to develop integrated solutions that jointly optimize schedules, crew pairings, and crew rosters for demand forecasts that are uncertain at the time rosters are published. This paper describes how these changes are reshaping the airline industry during COVID-19, explains why short lead times for bookings and uncertainty in demand volumes are expected to remain after COVID-19, and describes how the airline industry is innovating and developing new operations research approaches for handling uncertain and volatile demand. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Airline operations Airlines COVID-19 Crew planning Demand forecasting Schedule planning

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Versatile Test Reactor Open Digital engineering Ecosystem

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Insight 2025年第1期28卷 64-68页

作者： Ritter, Christopher Browning, Jeren Suyderhoud, Peter Hays, Ross Marshall, AnnMarie Han, Kevin Ashbocker, Taylor Darrington, John Nelson, Lee Idaho National Laboratory Department of Digital Systems & Engineering Versatile Test Reactor Program Idaho FallsID83401 United States Pivotal Solutions 3 Grace Avenue Suite 162 Great NeckNY11021 United States North Carolina State University Department of Civil and Environmental Engineering United States Department of Computer Science University of Illinois at Urbana-Champaign 205 N. Mathews Ave. UrbanaIL61801 United States

Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors made early in design can introduce silent errors. These errors can cascade causing unknown risk of complex engineering programs. The Versatile Test Reactor (VTR) Program uses digital-engineering principles for design, procurement, construction, and operation to reduce risk and improve efficiencies. Digital engineering is an integrated, model-based approach which connects proven digital tools such as building information management (BIM), project controls, and systems-engineering software tools into a cohesive environment. The VTR team hypothesizes using these principals can lead to similar risk and cost reductions and schedule efficiencies observed in other engineering industries. This research investigates the use of a digital engineering ecosystem in the design of a 300-MWt sodium-cooled fast reactor. This ecosystem was deployed to over 200 engineers and used to deliver the conceptual design of the VTR. We conclude that initial results show significant reductions in user latency (1000x at peak use), the possibility of direct finite-element-analysis (FEA) integrations to computer-aided design (CAD) tools, and nuclear reactor system design descriptions (SDDs) that we can fully link throughout design in data-driven requirements-management software. These early results led to the VTR maintaining milestone performance during the COVID-19 pandemic. Copyright © 2022 by Idaho National Laboratory. Published by INCOSE with permission.

关键词： Sodium-cooled fast reactors

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Multi-LSTM-Based Framework for Ambient Intelligence 39th

Multi-LSTM-Based Framework for Ambient Intelligence

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39th IMAC, A Conference and Exposition on Structural Dynamics, 2021

作者： Gulgec, Nur Sila Takáč, Martin Pakzad, Shamim N. Thornton Tomasetti San FranciscoCA United States Department of Industrial and Systems Engineering Lehigh University BethlehemPA United States Department of Civil and Environmental Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9783030771423

Bridge structures experience significant vibrations, repeated stress cycles, and deterioration during their life cycles. Thus, it is essential to accurately establish life cycle assessment which often needs collecting strain responses. Conventional way of measuring strain measurements is laboriously ineffective and expensive as more spatial information is desired. In the presented approach, inexpensively collected acceleration responses by using wireless sensor networks or mobile sensing are utilized to predict strain information and decrease the installation expenses of sensors systems, as well as enable crowdsourcing potential of the mobile sensing. The study employs a deep learning framework composed of fully connected (FC) and multiple Long Short-Term Memory (LSTM) layers to predict strain time series from the acceleration responses. The deep architecture achieves to learn the relationship between input and output by exploiting the temporal dependencies of them. In the evaluation of the method, acceleration data collected from a steel bridge is utilized to predict the strain time series. The learned architecture is tested on an acceleration time series that the structure has never experienced. The initial findings show that this study holds great potential to perform sensing of the structures with affordable and intelligent sensing systems. © 2022, The Society for Experimental Mechanics, Inc.

关键词： Structural health monitoring

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Beyond Capacity: Contractual Form In Electricity Reliability Obligations

SSRN

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SSRN 2023年

作者： Shu, Han Mays, Jacob Systems Engineering Cornell University Ithaca14850 United States Civil and Environmental Engineering Cornell University Ithaca14850 United States

Liberalized electricity markets often include resource adequacy mechanisms that require consumers to contract with generation resources well in advance of real-time operations. While administratively defined mechanisms have most commonly taken the form of a capacity obligation, efficient markets would feature a broad array of arrangements adapted to the risk profiles and appetites of market participants. This article considers how the financial hedge embedded in alternative resource adequacy contract designs can induce different responses from risk-averse investors, with consequences for the resource mix and market structure. We construct a stochastic equilibrium model describing a competitive market with incomplete risk trading and compute investment equilibria under different contracting regimes. Two policy recommendations result. First, to avoid creating inefficiency by crowding out other forms of risk sharing, system operators should allow resources contracted through other means to opt out of mandatory capacity mechanisms, with their contribution to those requirements subtracted from administratively defined demand curves. Second, if they wish to promote a single contractual form, regulators should consider replacing existing option-like capacity mechanisms with a shaped forward contract for energy. Beyond these recommendations, we discuss the tension that liberalized systems face in seeking to promote both reliability and competitive outcomes. © 2023, The Authors. All rights reserved.

关键词： Investments

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Assessment of freshwater ecosystem health condition based on fish habitats using a holistic modelling approach

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Discover Water 2025年第1期5卷 1-20页

作者： Anand, Vicky Oinam, Bakimchandra Singh, Shailesh Kumar Wieprecht, Silke Department of Civil Engineering National Institute of Technology Manipur India Department of Hydraulic Engineering and Water Resources Management Institute for Modeling Hydraulic and Environmental Systems University of Stuttgart Stuttgart Germany National Institute of Water and Atmospheric Research Christchurch New Zealand

Global biodiversity is largely dependent on aquatic habitats. The state of fish habitats and the health of the aquatic ecosystem as a whole are important indicators of environmental quality. In recent years, the implications of human interference and climate change on aquatic environments have gained significant attention. Hydrological modifications driven on by anthropogenic activities are steadily degrading flow conditions and threatening the abundance of fish in the geographic region, while simultaneously having an impact on the health of the watershed. Therefore, the integrated hydrological-hydrodynamic-habitat modelling under diverse flow regimes can be used to assess the eco-sustainability and habitat suitability of the fish species. This article categorizes the habitat variables of fish into three groups: eco-hydrological, hydrodynamic-water quality, and eco-biological. This categorization facilitates an understanding of the mechanisms by which each factor affects fish and their habitats. The study, as the first of its kind, provides an extensive review of eco-hydrological models, hydrodynamic-water quality models, and habitat models, along with their relevant influencing components. It proposes a scientific framework for evaluating the potential hazards of aquatic habitat degradation, with an emphasis on data-scarce regions. This strategy could potentially establish a scientific basis for the preservation and restoration of aquatic ecosystems.

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Blockchain and Web of Things for Structural Health Monitoring Applications: A Proof of Concept 19

Blockchain and Web of Things for Structural Health Monitorin...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Gigli, Lorenzo Sciullo, Luca Montori, Federico Marzani, Alessandro Di Felice, Marco University of Bologna Department of Computer Science and Engineering Italy University of Bologna Department of Civil Chemical Environmental and Materials Engineering Italy University of Bologna Advanced Research Center on Electronic Systems Ercole de Castro Italy

ISBN: (纸本)9781665431620

Interoperable and secure data management techniques are fundamental for most of large-scale Structural Health Monitoring (SHM) systems. Indeed, given the relevance of SHM critical measurements, data integrity must be protected against tampering or falsifications. In this paper, we propose a four-layer SHM architecture that allows to build an effective data pipeline from sensors to consumer applications, passing through the cloud. The architecture is built on top of the MODRON platform and exploits the recent advances of the W3C Web of Things (WoT) standard for interoperability. We then discuss how third-party services can take benefit of the W3C WoT architecture to retrieve the SHM critical data and to publish them on the Ethereum Blockchain through an SHM-specific Smart Contract, for data protection and traceability purposes. We test the effectiveness of the Smart Contract implementation in terms of latency and costs under simulated workloads. © 2022 IEEE.

关键词： Smart contract

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Assessing Transmission Risks of SARS-CoV-2 Omicron Variant in U.S. School Facilities and Mitigation Measures 22

Assessing Transmission Risks of SARS-CoV-2 Omicron Variant i...

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Proceedings of the Winter Simulation Conference

作者： Yifang Xu Shuai Li Qiang He Siyao Zhu Jiannan Cai Department of Civil and Environmental Engineering University of Tennessee Knoxville TN USA Department of Civil and Systems Engineering Johns Hopkins University Baltimore MD USA School of Civil & Environmental Engineering and Construction Management The University of Texas at San Antonio San Antonio TX USA

The emergence of the SARS-CoV-2 Omicron variant raises concerns for school operations worldwide. The Omicron variant spread faster than other variants that cause COVID-19, and breakthrough infections are reported in vaccinated people. Schools are hotbeds for the transmission of the highly contagious virus. Therefore it is crucial to understand the risks of Omicron transmission and the effectiveness of different measures to prevent the surge of infection cases. This study estimates the risks of airborne transmission and fomite transmission of Omicron variants using simulations and the data of 11,485 public and private schools in the U.S. It also analyzes the impact of different mitigation measures on limiting airborne transmission and fomite transmission risks in schools. It was found that the Omicron variant caused relatively high infection risks in schools. The risk of airborne transmission is nine times higher than fomite transmission. The effective mitigation measures can significantly decrease the transmission risk.

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Modeling the residual queue and queue-dependent capacity in a static traffic assignment problem

arXiv

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arXiv 2025年

作者： Fu, Hao Lam, William H.K. Ma, Wei Shi, Yuxin Jiang, Rui Sun, Huijun Gao, Ziyou School of Systems Science Beijing Jiaotong University Beijing China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong

The residual queue during a given study period (e.g., peak hour) is an important feature that should be considered when solving a traffic assignment problem under equilibrium for strategic traffic planning. Although studies have focused extensively on static or quasi-dynamic traffic assignment models considering the residual queue, they have failed to capture the situation wherein the equilibrium link flow passing through the link is less than the link physical capacity under congested conditions. To address this critical issue, we introduce a novel static traffic assignment model that explicitly incorporates the residual queue and queue-dependent link capacity. The proposed model ensures that equilibrium link flows remain within the physical capacity bounds, yielding estimations more aligned with data observed by traffic detectors, especially in oversaturated scenarios. A generalized link cost function considering queue-dependent capacity, with an additional queuing delay term is proposed. The queuing delay term represents the added travel cost under congestion, offering a framework wherein conventional static models, both with and without physical capacity constraints, become special cases of our model. Our study rigorously analyzes the mathematical properties of the new model, establishing the theoretical uniqueness of solutions for link flow and residual queue under certain conditions. We also introduce a gradient projection-based alternating minimization algorithm tailored for the proposed model. Numerical examples are conducted to demonstrate the superiority and merit of the proposed model and solution algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Evaluation of Connected Vehicle Identification-Aware Mixed Traffic Freeway Cooperative Merging

arXiv

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arXiv 2024年

作者： Liu, Haoji Jahedinia, Fatemeh Mu, Zeyu Park, B. Brian The Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22903 United States The Department of Civil and Environmental Engineering University of Virginia CharlottesvilleVA22903 United States

Cooperative on-ramp merging control for connected automated vehicles (CAVs) has been extensively investigated. However, they did neglect the connected vehicle identification process, which is a must for CAV cooperations. In this paper, we introduced a connected vehicle identification system (VIS) into the on-ramp merging control process for the first time and proposed an evaluation framework to assess the impacts of VIS on on-ramp merging performance. First, the mixed-traffic cooperative merging problem was formulated. Then, a real-world merging trajectory dataset was processed to generate dangerous merging scenarios. Aiming at resolving the potential collision risks in mixed traffic where CAVs and traditional human-driven vehicles (THVs) coexist, we proposed on-ramp merging strategies for CAVs in different mixed traffic situations considering the connected vehicle identification process. The performances were evaluated via simulations. Results indicated that while safety was assured for all cases with CAVs, the cases with VIS had delayed initiation of cooperation, limiting the range of cooperative merging and leading to increased fuel consumption and acceleration variations. Copyright © 2024, The Authors. All rights reserved.

关键词： Merging

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Multimodal Gas Detection Using E-Nose and Thermal Images:An Approach Utilizing SRGAN and Sparse Autoencoder

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Computers, Materials & Continua 2025年第5期83卷 3493-3517页

作者： Pratik Jadhav Vuppala Adithya Sairam Niranjan Bhojane Abhyuday Singh Shilpa Gite Biswajeet Pradhan Mrinal Bachute Abdullah Alamri Artificial Intelligence and Machine Learning Department Symbiosis Institute of TechnologySymbiosis International(Deemed)UniversityPune412115India Symbiosis Centre of Applied AI(SCAAI) Symbiosis International(Deemed)UniversityPune412115India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringUniversity of Technology SydneyUltimoNSW 2007Australia Department of Geology and Geophysics College of ScienceKing Saud UniversityRiyadh11362Saudi Arabia

Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time *** fusion of these modalities can result in the development of highly accurate diagnostic *** low-cost thermal imaging software produces low-resolution thermal images in grayscale format,hence necessitating methods for improving the resolution and colorizing the *** objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images,followed by a sparse autoencoder for colorization of thermal images and amultimodal convolutional neural network for gas detection using electronic nose and thermal *** dataset used comprises 6400 thermal images and electronic nose measurements for four classes.A multimodal Convolutional Neural Network(CNN)comprising an EfficientNetB2 pre-trainedmodel was developed using both early and late feature *** Super Resolution Generative Adversarial Network(SRGAN)model was developed and trained on low and high-resolution thermal *** autoencoder was trained on the grayscale and colorized thermal *** SRGAN was trained on lowand high-resolution thermal images,achieving a Structural Similarity Index(SSIM)of 90.28,a Peak Signal-to-Noise Ratio(PSNR)of 68.74,and a Mean Absolute Error(MAE)of *** autoencoder model produced an MAE of 0.035,a Mean Squared Error(MSE)of 0.006,and a Root Mean Squared Error(RMSE)of *** multimodal CNN,trained on these images and electronic nose measurements using both early and late fusion techniques,achieved accuracies of 97.89% and 98.55%,***,the proposed framework can be of great aid for the integration with low-cost software to generate high quality thermal camera images and highly accurate detection of gases in real-time.

关键词： Thermal imaging gas detection multimodal learning generative models autoencoders

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