Article
Automation & Control Systems
Lloyd MacKinnon, Christopher L. E. Swartz
Summary: Real-time optimization (RTO) is a valuable tool for economic optimization of chemical process systems. This paper extends the formulation of closed-loop dynamic RTO (CL-DRTO) to include uncertainty handling. A robust multi-scenario CL-DRTO scheme is introduced to model the dynamic behavior of the plant and its MPC system under uncertainty, and its performance is evaluated in nonlinear case studies.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Operations Research & Management Science
Maaike Hoogeboom, Yossiri Adulyasak, Wout Dullaert, Patrick Jaillet
Summary: The research proposes a robust vehicle routing problem with time window assignments (RVRP-TWA) that aims to simultaneously determine routes and time window assignments to minimize the expected travel time and the risk of violating time windows. The approach is based on estimating unknown travel time probability distributions using statistical data and solving the problem by iteratively generating subgradient cuts.
TRANSPORTATION SCIENCE
(2021)
Article
Economics
Li Zhang, Zhongshan Liu, Lan Yu, Ke Fang, Baozhen Yao, Bin Yu
Summary: This paper studies the routing optimization problem of shared autonomous electric vehicles (SAEVs) and proposes a branch and-price algorithm to solve it. By considering charging schedules, uncertain travel time, and uncertain service time, conservative and robust SAEV routes are designed. The algorithm performs well in instance testing and demonstrates the factors affecting SAEV service through sensitivity analysis.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Automation & Control Systems
Honggui Han, Jiacheng Zhang, Ying Hou, Junfei Qiao
Summary: To achieve excellent operational performance in wastewater treatment, a kernel-density-estimation-based robust optimal control (KDE-ROC) method is proposed. This method addresses uncertainties in operational optimal objectives and utilizes a data-driven prediction strategy to construct these objectives. An adaptive neural network controller is developed to track the optimal set-points of process variables, resulting in improved control performance. The effectiveness of KDE-ROC is demonstrated through comparisons with other optimal control strategies.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
J. G. Hoffer, S. Ranftl, B. C. Geiger
Summary: This article discusses how to find an input such that the output of a stochastic black box function is as close as possible to a target value. It fills the gap in current approaches by deriving acquisition functions for common criteria and demonstrating their compatibility with certain extensions of Gaussian processes. The experiments show that these derived acquisition functions can outperform classical Bayesian optimization.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Management
Chungmok Lee
Summary: This study focuses on optimization problems with uncertain coefficients and proposes an F-optimal solution algorithm, which guarantees to remain the best solution even after additional F probings of uncertain data. The research shows that the proposed approach can find the true optimal solutions at very high percentages, even with small numbers of probings.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Automation & Control Systems
Jiahui Duan, Zhenan He, Gary G. Yen
Summary: This article focuses on the robust multiobjective optimization approach for the vehicle routing problem with time windows under uncertainty. By designing a new form of disturbance on travel time and incorporating an advanced encoding and decoding scheme, the proposed algorithm is able to generate enough robust solutions and ensure the optimality of these solutions, as validated by experimental results.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Operations Research & Management Science
Enrico Bartolini, Dominik Goeke, Michael Schneider, Mengdie Ye
Summary: This study focuses on the Traveling Salesman Problem with Time Windows (TSPTW) under travel time uncertainty, proposing an exact algorithm based on column generation and dynamic programming to address robust TSPTW under both knapsack- and cardinality-constrained travel time uncertainty. The algorithm successfully solves instances with up to 80 customers, and investigates the trade-off between service quality and cost resulting from the two uncertainty sets.
TRANSPORTATION SCIENCE
(2021)
Article
Automation & Control Systems
Yunfan Zhang, Feng Liu, Yifan Su, Yue Chen, Zhaojian Wang, Joao P. S. Catalao
Summary: This paper investigates a class of two-stage robust optimization problems that involve decision-dependent uncertainties. A novel iterative algorithm based on Benders dual decomposition is proposed, which guarantees the computational tractability, robust feasibility and optimality, and convergence performance with theoretical proof. Four motivating application examples that feature decision-dependent uncertainties are provided.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Management
Simon Thevenin, Oussama Ben-Ammar, Nadjib Brahimi
Summary: Supplier reliability is crucial for manufacturing companies, and to mitigate the impact of delivery delays and lead time uncertainty, companies employ strategies such as diversification and multi-sourcing. This study explores the use of robust optimization to address the integrated problem of supplier selection and lot-sizing under lead time uncertainty, aiming to minimize total costs while considering supplier reliability and prices.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Alexandre Cesar Balbino Barbosa Filho, Sergio Mauro da Silva Neiro
Summary: The paper presents a robust optimization framework that combines various concepts and principles to provide reliable solutions. The framework is applicable to both linear and nonlinear mathematical models, as well as discrete and continuous optimization problems. Numerical simulations validate the high tractability and performance of the framework.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Mechanics
Aybike Ozyuksel Ciftcioglu, Betul Ustuner, Erkan Dogan, Sachi Arafat, Amir Hussain
Summary: This research presents a comprehensive comparative analysis of optimization techniques for achieving the optimal design of cellular beams, and includes modeling and analysis of the optimally designed beams using finite element software.
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(2023)
Article
Engineering, Electrical & Electronic
Kaiping Qu, Xiaodong Zheng, Xiaoqiang Li, Chaoxian Lv, Tao Yu
Summary: This paper proposes a novel stochastic robust real-time power dispatch model considering wind uncertainty. The model enhances system security and cost efficiency by incorporating automatic power generation control and affinely adjustable robust optimization. The Nataf conversion-based three-point estimate method achieves accurate formulation and stable computation performance.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Energy & Fuels
Marcos Tostado-Veliz, Hany M. Hasanien, Rania A. Turky, Ahmad Rezaee Jordehi, Seyed Amir Mansouri, Francisco Jurado
Summary: On-board batteries from electric vehicles can be used as an auxiliary source of energy in domestic installations. Home Energy Management systems are crucial in future smart grids to efficiently use energy in residential applications. However, managing uncertainties caused by intermittent renewable generation, uncertain prices, and behaviors of electric vehicles requires further research. This paper proposes a fully robust Home Energy Management model that accounts for all uncertainties and validates its effectiveness through a case study.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Management
Pengfei Zhang, Diwakar Gupta
Summary: In this paper, a new uncertainty set is proposed for robust models of linear optimization problems. The statistical properties of continuous and independent random variables are studied using the Probability Integral Transform. A new uncertainty set is constructed by placing constraints on the order statistics of random variables. The order statistic uncertainty set is shown to outperform other uncertainty sets in a robust portfolio construction problem when the sample size is small and the correlation between random variables is low.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Enrique Benavent, Mercedes Landete, Enrique Mota, Gregorio Tirado
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2015)
Article
Biodiversity Conservation
Begona Vitoriano, J. Tinguaro Rodriguez, Gregorio Tirado, F. Javier Martin-Campo, M. Teresa Ortuno, Javier Montero
HUMAN AND ECOLOGICAL RISK ASSESSMENT
(2015)
Article
Computer Science, Artificial Intelligence
Jose M. Ferrer, M. Teresa Ortuno, Gregorio Tirado
JOURNAL OF HEURISTICS
(2016)
Article
Operations Research & Management Science
Gregorio Tirado, Lars Magnus Hvattum
ANNALS OF OPERATIONS RESEARCH
(2017)
Article
Engineering, Industrial
Gregorio Tirado, Lars Magnus Hvattum
FLEXIBLE SERVICES AND MANUFACTURING JOURNAL
(2017)
Article
Management
Jose M. Ferrer, F. Javier Martin-Campo, M. Teresa Ortuno, Alfonso J. Pedraza-Martinez, Gregorio Tirado, Begona Vitoriano
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2018)
Article
Operations Research & Management Science
Are Denstad, Einar Ulsund, Marielle Christiansen, Lars Magnus Hvattum, Gregorio Tirado
Summary: The banking industry is facing various challenges due to regulatory changes and technological advancements; the redesign of ATM networks to adapt to the increased use of electronic payment methods is crucial for cost reduction and network performance.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Enrique Benavent, Mercedes Landete, Juan Jose Salazar-Gonzalez, Gregorio Tirado
EXPERT SYSTEMS WITH APPLICATIONS
(2019)
Article
Operations Research & Management Science
Jose L. Arroyo, Angel Felipe, M. Teresa Ortuno, Gregorio Tirado
CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH
(2020)
Article
Management
Adolfo Urrutia-Zambrana, Gregorio Tirado, Alfonso Mateos
Summary: This paper introduces a variable neighborhood search algorithm to solve the generalized orienteering problem, outperforming all previous metaheuristics by reducing the number of neighborhoods and precalculating scores. It discovered 35 new best solutions in the case studies and improved information on other best-known solutions by correcting errors and adding new real data case studies from popular tourist cities in Spain.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2021)
Article
Mathematics
Inmaculada Flores, M. Teresa Ortuno, Gregorio Tirado, Begona Vitoriano
Article
Mathematics
Jose M. Ferrer, M. Teresa Ortuno, Gregorio Tirado
Article
Mathematics
Lars Magnus Hvattum, Gregorio Tirado, Angel Felipe
Article
Education & Educational Research
Jose Luis Arroyo-Barriguete, Gregorio Tirado, Ignacio Mahillo-Fernandez, Pedro Jose Ramirez
REVISTA DE EDUCACION
(2020)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
