Article
Computer Science, Interdisciplinary Applications
Gaukhar Shaimerdenova, Hakon Hoel, Raul Tempone
Summary: In this work, a highly efficient filtering method called multi-index EnKF (MIEnKF) is proposed by combining ideas from multi-index Monte Carlo and ensemble Kalman filtering. The MIEnKF method is based on independent samples of four-coupled EnKF estimators on a multi-index hierarchy of resolution levels, serving as an extension of the multilevel EnKF (MLEnKF) method developed by the same authors in 2020. Numerical verifications demonstrate that MIEnKF is more tractable and efficient than EnKF and MLEnKF.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Automation & Control Systems
Amirhossein Taghvaei, Prashant G. Mehta
Summary: Controlled interacting particle systems such as the ensemble Kalman filter (EnKF) and the feedback particle filter (FPF) utilize feedback control laws for Bayesian update steps. This article focuses on the uniqueness of control laws in these algorithms by formulating the filtering problem as an optimal transportation problem and deriving an explicit formula for the optimal control law in the linear Gaussian setting. Empirical approximation of the mean-field control law leads to a finite-N controlled interacting particle algorithm, with demonstrated convergence properties similar to the Kalman filter.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Review
Engineering, Industrial
Giray Okten, Yaning Liu
Summary: Randomized quasi-Monte Carlo methods are becoming more popular in applications due to their faster convergence rate and the availability of simple statistical tools for analyzing estimation errors.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Geosciences, Multidisciplinary
Sangeetika Ruchi, Svetlana Dubinkina, Jana de Wiljes
Summary: This study introduces a method for identifying unknown parameters in high-dimensional and nonlinear environments, which has higher accuracy but also higher computational complexity. The method shows certain advantages in robustness and performance compared to ensemble Kalman inversion.
NONLINEAR PROCESSES IN GEOPHYSICS
(2021)
Article
Construction & Building Technology
Dapeng Niu, Lei Guo, Xiaolin Bi, Di Wen
Summary: This paper presents a decision-making method for elevator parts' maintenance, which determines a reasonable maintenance period through analyzing historical fault data and establishing a mixed failure rate model. The study shows that a reasonable maintenance period can reduce the risk of equipment failure, save costs, and avoid excessive waste of resources.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Automation & Control Systems
Michael Hoffman, Eunhye Song, Michael P. Brundage, Soundar Kumara
Summary: This study proposes a two-stage approach to address the problem of maintenance resource allocation, optimizing a static maintenance policy using a genetic algorithm initially, and then improving the policy online through Monte Carlo tree search to maximize production volume and resolve conflicts between maintenance and production objectives.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Saeede Mohammadi Daniali, Alireza Khosravi, Pouria Sarhadi, Fatemeh Tavakkoli
Summary: This article develops an automatic vehicle parallel parking algorithm that includes path planning, controller design, and state estimation. The path is planned using clothoid sequences and a straight line, without the need for stopping the car to reorient the wheels. The control inputs are a function of traveled distance. The proposed technique aims to improve existing methods in terms of parking duration, space requirements, and path continuity, and it is evaluated using Monte Carlo simulations.
Article
Acoustics
Hamed Amini Tehrani, Ali Bakhshi, Tony T. Y. Yang
Summary: The study proposes a Bayesian multiple modeling approach for dynamic structural identification, aiming to improve data fitting precision, reduce dimensionality of structural unknowns, and enhance stability and convergence rate of the identification algorithm. Experimental validation on nonlinear systems with a large number of unknowns demonstrates the effectiveness and performance of the method.
JOURNAL OF VIBRATION AND CONTROL
(2021)
Article
Statistics & Probability
Chaofan Huang, V. Roshan Joseph, Simon Mak
Summary: Monte Carlo methods are widely used for approximating complicated, multidimensional integrals for Bayesian inference. Population Monte Carlo (PMC) is an important class of Monte Carlo methods that adapts a population of proposals to generate weighted samples approximating the target distribution. To address computational limitations of PMC when evaluating the target distribution is expensive, we propose a new method, Population Quasi-Monte Carlo (PQMC), which incorporates Quasi-Monte Carlo ideas and introduces importance support points resampling and efficient covariance adaptation strategies within the sampling and adaptation steps of PMC.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2022)
Article
Automation & Control Systems
Adrian G. Wills, Thomas B. Schon
Summary: This paper introduces a novel quasi-Newton algorithm for stochastic optimization, extending the rapid convergence and computational attractiveness seen in deterministic optimization problems. The algorithm learns second-order information based on observing first-order gradients, with a highly flexible model for the Hessian. A stochastic counterpart to standard line-search procedures is proposed and demonstrated to be useful in maximum likelihood identification for general nonlinear state space models.
Article
Computer Science, Theory & Methods
Hamza Ruzayqat, Neil K. Chada, Ajay Jasra
Summary: This article discusses the application of multilevel Monte Carlo in the estimation of normalizing constants, specifically focusing on the multilevel ensemble Kalman-Bucy filter (MLEnKBF) method. Numerical results are provided to validate the approach, and parameter estimation is tested on atmospheric science models such as the stochastic Lorenz 63 and 96 model.
STATISTICS AND COMPUTING
(2022)
Article
Mathematics, Applied
Dan Crisan, Michael Ghil
Summary: Extensive numerical evidence demonstrates that assimilating observations has a stabilizing effect on unstable dynamics, both in numerical weather prediction and other fields. In this paper, we employ mathematically rigorous methods to explain the underlying reasons behind this phenomenon. Our stabilization results do not necessitate a complete set of observations and we provide examples where observing only the unstable degrees of freedom of the model is sufficient.
Article
Automation & Control Systems
Sifan Liu, Art B. Owen
Summary: Our study demonstrates that using randomized quasi-Monte Carlo sampling can improve optimization in machine learning problems, especially in cases where second order methods are not effective. In cases where the sampling method has a low root mean squared error, RQMC can achieve better optimization results.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Xinlong Li, Yan Ran, Baojia Chen, Fafa Chen, Yunfei Cai, Genbao Zhang
Summary: This paper proposes an optimization model for scheduled maintenance (SM) and condition-based maintenance (CBM) at the unit-level and analyzes the associated maintenance costs. An optimization algorithm based on Monte Carlo simulation is designed to solve the model. The results show that the maintenance strategy presented in this paper can significantly reduce maintenance costs.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Pierre L'Ecuyer, Florian Puchhammer, Amal Ben Abdellah
Summary: Using conditional Monte Carlo, we can obtain a random conditional density that serves as an unbiased estimator of the true density. By taking the average of independent replications, we can achieve a faster convergence rate for the density estimator. Combining this new estimator with randomized quasi-Monte Carlo further improves the error and convergence rate.
INFORMS JOURNAL ON COMPUTING
(2022)
Article
Automation & Control Systems
Yuantao Yao, Minghan Yang, Jianye Wang, Min Xie
Summary: This article proposes an end-to-end, deep hybrid network-based, short-term, multivariate time-series prediction framework for industrial processes. The framework extracts nonlinear variate correlation features using the maximal information coefficient, eliminates data uncertainty with a convolutional neural network, achieves step-ahead prediction using a bidirectional gated recurrent unit network, and optimizes the model's learning rate with an optimized Bayesian optimization method. The comparison with other state-of-the-art methods demonstrates the superiority of the proposed framework in noisy IIoT environments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Manufacturing
Feng Zhu, Xiaodong Jia, Wenzhe Li, Min Xie, Lishuai Li, Jay Lee
Summary: Unit-to-unit variation is a challenge for Fault Detection and Classification (FDC) in the semiconductor industry. Existing methods lack an evaluation of data transferability among chambers. This research proposes a methodology for data transferability evaluation and sensor screening, utilizing Time Series Alignment Kernel (TSAK) and Multidomain Discriminant Analysis (MDA) algorithm for domain generalization and feature extraction, Fisher's criterion ratios are computed for quantifying knowledge transferability, and Recursive Feature Elimination (RFE) is used for sensor selection.
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
(2023)
Article
Engineering, Industrial
Yuqing Zhang, Min Xie, Yihai He, Xiao Han
Summary: This paper proposes a capability-based approach for predicting the remaining useful life (RUL) of machining tools, which aims to evaluate the state and RUL of tools to ensure product quality. By developing physics-based and data-driven models to assess the quality capability of tools, the effectiveness and accuracy of the proposed approach are verified.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Zhiying Wu, Zhe Wang, Yan Wang, Junlin Xiong, Min Xie
Summary: This paper investigates the dynamic event-triggered predictive control problem for discrete-time networked control systems under deception attacks. A new dynamic event-triggered scheme is proposed, and the Luenberger observer and networked predictive control method are used. Sufficient conditions are established to guarantee the stability of the closed-loop systems. The effectiveness of the approach is validated through experiments.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Wei Fan, Zhenqiang Chen, Yongxiang Li, Feng Zhu, Min Xie
Summary: Condition monitoring is crucial for ensuring the reliability and safety of rotating machinery. The construction of a health index (HI) is essential for detecting faults and assessing degradation. However, existing classical HIs have limitations in dealing with strong noise and incipient faults. To address these shortcomings, this paper proposes a reinforced noise resistant correlation method that effectively suppresses noise interference and detects incipient faults.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Chunyan Ling, Way Kuo, Min Xie
Summary: This study reviews the advantages and disadvantages of using surrogate models to streamline reliability-based design optimization (RBDO), as well as discussing the problems that need to be solved.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Electrical & Electronic
Zhiying Wu, Yan Wang, Aibo Zhang, Junlin Xiong, Min Xie
Summary: This paper introduces an event-triggered control method in unmanned aerial vehicle (UAV) systems based on cognitive radio networks, taking into consideration the deception attacks. By proposing a Quality of Service (QoS)-dependent event-triggered scheme (ETS), the system parameters are adjusted to ensure stability and performance.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Chun Fai Lui, Ahmed Maged, Min Xie
Summary: With the development of AM technology, quality inspection has become a crucial research topic. This article proposes a novel IFSSL model that utilizes self-supervised learning to effectively inspect the quality of AM products. The model leverages defect-relevant feature extraction and image fusion to guide machine vision towards relevant regions and detect faults automatically.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Engineering, Industrial
Xuerong Ye, Qisen Sun, Ruishi Lin, Cen Chen, Min Xie, Guofu Zhai, Rui Kang
Summary: An improved reliability prediction method for DC-link electrolytic capacitors is proposed in this article to address the inadequate consideration of the self-acceleration effect in existing methods. The degradation under dynamic stress is obtained through cumulative computations and converted into degradation rate models, overcoming computational challenges and improving accuracy. The practicality and accuracy of the proposed method are demonstrated through a case study.
QUALITY ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Dong Li, Yiqi Liu, Daoping Huang, Chun Fai Lui, Min Xie
Summary: This article proposes an adversarial transfer learning (ATL) methodology to enhance soft sensor learning, which includes hierarchical transfer learning algorithm, adversarial learning network, and Granger causality analysis (GCA). Experimental results demonstrate that the ATL-based soft sensor can achieve more accurate prediction, and the GCA-based rationale analyzer can provide a visual explanation for the corresponding model and prediction results.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Hardware & Architecture
Lechang Yang, Chenxing Wang, Chunyan Ling, Min Xie
Summary: This article proposes a survival signature-based reliability framework for an imprecise multistate system (IMSS) to address the challenges of reliability evaluation for complex systems with imprecise parameters. The framework defines the survival signature and calculates the multistate survival functions based on the combination of states of composing elements. A simulation method is developed for probability estimation when imprecision is involved. An approximate Bayesian computation method with a Jensen-Shannon divergence-based kernel is developed to perform stochastic model updating and calibrate imprecise parameters. The proposed framework is validated with a numerical case of a typical bridge system and a real application example.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Automation & Control Systems
Xingchen Liu, Zhiyong Hu, Xin Wang, Min Xie
Summary: This paper studies the capacity degradation of lithium-ion batteries and analyzes the effects of cycling aging and calendar aging on battery degradation. The concept of cumulative uninterrupted cycling duration (CUCD) is introduced to capture the coupling effect between these two aging sources, and the drift rate of cycling aging is modeled using a monotonic spline. The effectiveness and superiority of the model are validated through numerical and real case studies.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Xingchen Liu, Xin Wang, Min Xie, Zhisheng Ye
Summary: Degradation analysis is crucial for system health management and remaining useful life prediction. This study proposes a framework for degradation state estimation based on distributionally robust optimization, which addresses the challenges of parameter uncertainty and measurement outlier, leading to more accurate evaluation of health status.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Min Wang, Min Xie, Yanwen Wang, Maoyin Chen
Summary: In this article, a deep quality monitoring network (DQMNet) is developed for the detection of quality-related incipient faults. DQMNet uses feature extraction and Bayesian inference to extract hidden information and construct statistics, demonstrating its superiority through numerical simulation and benchmark data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Yi Ding, He Li, Feng Zhu, Zhe Wang, Weiwen Peng, Min Xie
Summary: This article proposes a novel semi-supervised method for constructing failure knowledge graphs based on maintenance logs. The method extracts hidden contextual information from maintenance records and constructs failure items and their relationships to provide decision support. The feasibility and superiority of the method are validated using real wind farm data.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
