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作者： Sobhanan, Abhay Park, Junyoung Park, Jinkyoo Kwon, Changhyun Department of Industrial and Management Systems Engineering University of South Florida TampaFL33620 United States Department of Industrial and Systems Engineering KAIST Daejeon34141 Korea Republic of OMELET Daejeon34141 Korea Republic of

When vehicle routing decisions are intertwined with higher-level decisions, the resulting optimization problems pose significant challenges for computation. Examples are the multi-depot vehicle routing problem (MDVRP), where customers are assigned to depots before delivery, and the capacitated location routing problem (CLRP), where the locations of depots should be determined first. A simple and straightforward approach for such hierarchical problems would be to separate the higher-level decisions from the complicated vehicle routing decisions. For each higher-level decision candidate, we may evaluate the underlying vehicle routing problems to assess the candidate. As this approach requires solving vehicle routing problems multiple times, it has been regarded as impractical in most cases. We propose a novel deep learning-based approach called the genetic algorithm with neural cost predictor to tackle the challenge and simplify algorithm developments. For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pretrained graph neural network without actually solving the routing problems. In particular, our proposed neural network learns the objective values of the HGS-CVRP open-source package that solves capacitated vehicle routing problems. Our numerical experiments show that this simplified approach is effective and efficient in generating high-quality solutions for both MDVRP and CLRP and that it has the potential to expedite algorithm developments for complicated hierarchical problems. We provide computational results evaluated in the standard benchmark instances used in the literature. © 2024 INFORMS.

关键词： Genetic algorithms

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants 6

Optimal coordination of Production, Logistics, and Maintenan...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Zerrad, Raoua Diaz, Kamar Mechkouri, Meriem Hayani Sefiani, Naoufal Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaadi University Tangier Morocco Department of Industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Inventory control

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Tomography of Quantum States From Structured Measurements via Quantum-Aware Transformer

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IEEE Transactions on Cybernetics 2025年第6期55卷 2571-2582页

作者： Ma, Hailan Sun, Zhenhong Dong, Daoyi Chen, Chunlin Rabitz, Herschel University of New South Wales School of Engineering and Technology CanberraACT2600 Australia The Australian National University School of Engineering CanberraACT2601 Australia University of Technology Sydney Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology SydneyNSW2007 Australia Nanjing University School of Management and Engineering Department of Control and Systems Engineering Nanjing210093 China Princeton University Department of Chemistry PrincetonNJ08544 United States

Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices. However, the specific structure of quantum measurements for characterizing a quantum state has been neglected in previous work. In this article, we explore the similarity between highly structured sentences in natural language and intrinsically structured measurements in QST. To fully leverage the intrinsic quantum characteristics involved in QST, we design a quantum-aware transformer (QAT) model to capture the complex relationship between measured frequencies and density matrices. In particular, we query quantum operators in the architecture to facilitate informative representations of quantum data and integrate the Bures distance into the loss function to evaluate quantum state fidelity, thereby enabling the reconstruction of quantum states from measured data with high fidelity. Extensive simulations and experiments (on IBM quantum computers) demonstrate the superiority of the QAT in reconstructing quantum states with favorable robustness against experimental noise. © 2013 IEEE.

关键词： Quantum computers

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Integrating Real-Time Machine Learning and Dynamic Charging for Electric Vehicle Routing 59

Integrating Real-Time Machine Learning and Dynamic Charging ...

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59th Annual Conference on Information Sciences and systems, CISS 2025

作者： Ghahfarokhi, Mandana Farhang Park, Hyoshin Pandey, Venktesh Yoon, Gyugeun Old Dominion University Department of Engineering Management and Systems Engineering NorfolkVA United States North Carolina A and T State University Department of Civil Architectural and Environmental Engineering GreensboroNC United States Seoul National University Department of Civil Engineering Seoul Korea Republic of

ISBN: (纸本)9798331513269

As electric vehicles (EVs) gain popularity, the demand for efficient EV routing and innovative charging solutions is increasing. This study proposes a novel integration of Temporal Multimodal Multivariate Learning (TMML) and the Time-Dependent Shortest Path (TDSP) algorithm to dynamically optimize EV routing while incorporating stationary and wireless charging systems. TMML continuously updates travel time distributions based on real-time observations, reducing uncertainties and enabling more accurate predictions. These updated distributions are utilized by TDSP to determine the most efficient routes, considering time-dependent travel times, and the availability of charging infrastructure. The methodology integrates Mobile Energy Distributors (MEDs) and Dynamic Inductive Charging (DIC). MED dynamically dispatches portable charging units, while DIC enables on-the-move charging through road-embedded systems. Real-world data from the Washington, DC metropolitan area, sourced from Regional Integrated Transportation Information System (***), provides granular insights into traffic patterns and charging infrastructure performance. This hybrid system operates by extracting feasible routes, evaluating them using TMML and TDSP, and selecting the optimal path to minimize travel time, waiting time, and charging costs. © 2025 IEEE.

关键词： Travel time

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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Computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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Inconsistent Interpretations of an "-ility" in Practice: A Survey of Affordability Definitions within NASA MSFC

Inconsistent Interpretations of an "-ility" in Practice: A S...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： White, Christopher Eaton, Casey Yeazitzis, Taylor Perner, David Falcon, Amelia Mesmer, Bryan Weger, Kristin Thomas, L. Dale Department of Industrial and Systems Engineering and Engineering Management The University of Alabama in Huntsville HuntsvilleAL35899 United States Department of Psychology The University of Alabama in Huntsville HuntsvilleAL35899 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

systems engineering often employs "-ilities", such as affordability, to represent various aspects of a system. While these "-ilities" are widely used, exact definitions for the terms can vary between individuals. While most definitions of affordability include some cost notion, only a subset balance cost against a benefit. Additionally, some definitions treat affordability as a binary constraint, a system either is affordable or it is not. Others define it in more continuous terms which could be treated as an objective to be maximized. While multiple definitions can be readily observed from different sources, the degree to which definitions for a concept such as affordability differs within a single organization has not been thoroughly studied. This paper presents results from a survey completed by 172 employees of NASA Marshall Space Flight Center. Definitions of affordability from each participant are categorized ac-cording to how cost and benefit notions are used in the definition. Eight different categories of definitions were observed;however, 86 of the 172 definitions were categorized as involving a cost constraint with no clear benefit component. Due to the variety of definitions of affordability observed, practitioners should be cautious about assuming what stakeholders and fellow decision makers mean when using the term affordability. The use of affordability definitions categorized as involving only a cost constraint may result in the elimination of alternatives in a design space, rather than driving decision makers to continuously improve affordability. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation

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Computers, Materials & Continua 2025年第5期83卷 3369-3388页

作者： Hamza Murad Khan Anwar Khan Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani Department of Electronics University of PeshawarPeshawar25120KPKPakistan Department of Project Management(L.A.D.L) Higher Polytechnic School(S.G.V)Universidad Europea del AtlanticoSantander39011Spain Faculty of Engineering Universidad Internacional IberoamericanaCampeche24560Mexico Faculty of Engineering Universidade Internacional do CuanzaCuitoEN250BieAngola Department of Project Management Universidad Internacional IberoamericanaCampeche24560Mexico Department of Project Management Department of Management SciencesUniversid de La RomanaLa Romana22000Dominican Reublic Information Systems Department College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering&Computer ScienceJazan UniversityJazan45142Saudi Arabia

Traffic forecasting with high precision aids Intelligent Transport systems(ITS)in formulating and optimizing traffic management *** algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational *** address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning *** Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad *** ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of *** also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction *** proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the *** traffic data is then input to the LSTM model with tuned parameters to perform the traffic *** three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM *** results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.

关键词： Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model

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Research on De-identification Applications of LLMs in Medical Records

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International Journal of Network Security 2025年第1期27卷 213-222页

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yang-Shu Wu, Nan-I Lin, Iuon-Chang Department of Computer Science & Information Engineering Asia University 500 Lioufeng Rd. Wufeng Taichung41354 Taiwan Department of Management Information Systems National Chung Hsing University Taiwan Department of Information Management Lunghwa University of Science and Technology Taoyuan33306 Taiwan

The primary objective of this research is to develop and promote efficient and secure de-identification technology to address the application of sensitive personal information in healthcare big data. Considering the outstanding performance of large language models (such as GPT) in understanding text, we aim to utilize these advanced models to process medical records. Compared to traditional methods such as manual processing, rule-based, or machine learning methods, large language models can more effectively de-identify unstructured medical record data. We can enhance the recognition and de-identification accuracy of personal identification information in medical records through deep learning techniques. Additionally, the contextual understanding capabilities of large language models can comprehensively process medical record data, ensuring that the de-identification process does not affect the integrity and availability of medical information. Therefore, the research methodology will include model training, optimization, and validation to ensure that the developed de-identification technology achieves efficient and reliable results in practical applications. We explore different large language models, considering the diversity of personal medical data to address various de-identification needs in different medical record data. In this article, we propose three research issues for de-identification: 1) Research on the application of deep language models and de-identification technology in medical cases;2) optimization of prompt engineering to enhance de-identification of medical cases by large language models;3) Optimization and promotion of de-identification technology for practical applications in the medical. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Electronic health record

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Transforming Education with AI and Robotics: Potential, Perceptions, and Infrastructure Needs 21st

Transforming Education with AI and Robotics: Potential, Pe...

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21st European, Mediterranean, and Middle Eastern Conference on Information systems, EMCIS 2024

作者： Vasou, Marina Kyprianou, Georgios Amanatiadis, Angelos Chatzichristofis, Savvas A. Intelligent Systems Laboratory Neapolis University Pafos Paphos Cyprus Department of Production and Management Engineering Democritus University of Thrace Komotini Greece

ISBN: (纸本)9783031813214

The rapid rise of artificial intelligence (AI) is reshaping various industries, with education emerging as a pivotal field ripe for transformation. AI’s ability to analyze vast amounts of data, recognize patterns and make predictive decisions positions it as a game-changer in the educational sector. This study investigates the integration of AI and robotic systems in education, examining their potential to revolutionize learning experiences and outcomes. AI transforms education by enabling personalized learning paths, enhancing student engagement through adaptive learning platforms, and providing real-time feedback and assessment. It facilitates differentiated instruction tailored to individual student needs and fosters an environment where students can learn at their own pace. The research, involving a survey of 248 respondents from Cyprus and Greece, highlights a strong correlation between higher educational attainment and greater awareness and appreciation of AI’s role in education. There is significant support among educators and students for integrating AI into educational practices, underscoring a readiness to embrace these technologies for improved educational outcomes. However, the study also identifies critical challenges, including inadequate infrastructure and the need for substantial financial support to fully leverage AI’s potential. This research emphasizes the transformative impact of AI in education and advocates for strategic investments to overcome existing barriers and enhance the educational landscape through advanced technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Artificial Intelligence-Based Cybersecurity for the Metaverse: Research Challenges and Opportunities

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 1008-1052页

作者： Awadallah, Abeer Eledlebi, Khouloud Zemerly, Mohamed Jamal Puthal, Deepak Damiani, Ernesto Taha, Kamal Kim, Tae-Yeon Yoo, Paul D. Raymond Choo, Kim-Kwang Yim, Man-Sung Yeun, Chan Yeob Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Indian Institute of Management Bodh Gaya IT Systems and Analytics Bodh Gaya824234 India Khalifa University Civil and Environmental Engineering Department Abu Dhabi United Arab Emirates University of London Department of Computer Science and Information Systems LondonWC1E 7HX United Kingdom The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States Korea Advanced Institute of Science and Technology Department of Nuclear and Quantum Engineering Daejeon34141 Korea Republic of

The metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions. © 1998-2012 IEEE.

关键词： Cybersecurity

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