Article
Computer Science, Artificial Intelligence
Kirti Sharma, Vishnu Pratap Singh, Ali Ebrahimnejad, Debjani Chakraborty
Summary: Various optimization approaches have been developed and used for generating optimal solutions for different industry related optimization problems. The semantic representation of imprecise coefficients and various types of uncertainties arising in real life optimization problems are still a challenging task and require attention of academicians as well as professionals.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Agronomy
Youzhi Wang, Shanshan Guo, Qing Yue, Xiaomin Mao, Ping Guo
Summary: This study developed a distributed AquaCrop simulation nonlinear multi-objective dependent chance programming method to address uncertainties in irrigation water resources management. By analyzing and optimizing 134 decision-making units for spatial heterogeneities, handling fuzzy goals and tradeoffs relationships between objectives, two groups of Pareto optimal solutions were obtained. Results showed that weather conditions and soil types significantly affect system outputs, and different water allocation patterns during growth periods impact yield and water productivity.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Engineering, Environmental
Youzhi Wang, Ping Guo
Summary: A copula-measure based interval multi-objective multi-stage stochastic chance-constrained programming (CMIMOMSP) model is proposed for water consumption optimization. It introduces multi-objective programming to improve the traditional stochastic chance-constrained programming by considering relationships among various factors. The model is applied to a case study in the Heihe River Basin, showing different impacts of optimistic-pessimistic factors on water allocation for different sectors.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Computer Science, Interdisciplinary Applications
Somayeh Khezri, Salman Khodayifar
Summary: This study focuses on the minimum cost multi-commodity network flow (MCNF) problem in network optimization, which is complicated by the presence of uncertain parameters. A multi-objective approach is proposed, using random variables and Archimedean copula to model the uncertain parameters. The problem is then converted into a certain multi-objective problem using fuzzy programming and solved using second-order cone programming. The results demonstrate the effectiveness of the proposed approaches in solving large-scale network problems efficiently.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
C. Haider, F. O. de Franca, B. Burlacu, G. Kronberger
Summary: We describe and analyze algorithms for shape-constrained symbolic regression, which incorporate prior knowledge about the shape of the regression function. These algorithms are tested on physics models and compared to previous results achieved with single objective algorithms. The results show that the multi-objective algorithms can find mostly valid models even when using a soft-penalty approach. NSGA-II achieves the best overall rankings on instances with high noise.
APPLIED SOFT COMPUTING
(2023)
Article
Energy & Fuels
Alexander Thebelt, Calvin Tsay, Robert M. Lee, Nathan Sudermann-Merx, David Walz, Tom Tranter, Ruth Misener
Summary: This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems with heterogeneous variable spaces. It demonstrates competitive performance and sampling efficiency in real-world applications with limited evaluation budgets compared to other state-of-the-art tools.
Article
Green & Sustainable Science & Technology
Xian Huang, Wentong Ji, Xiaorong Ye, Zhangjie Feng
Summary: This paper proposes a multi-objective planning model based on chance-constrained programming to optimize the configuration of self-consistent energy systems for expressway electricity demand in no-grid areas with 100% renewable energy supply. The uncertainties of electric load and renewable energy sources are modeled using Monte Carlo Simulation and the backward reduction method. The Pareto solutions are optimized using the non-dominated sorted genetic algorithm-II, and the best solution is determined through the CRITIC and TOPSIS approach. The results from case studies demonstrate the effectiveness of the proposed method in enhancing system robustness and meeting power demand under confidence scenarios.
Article
Operations Research & Management Science
Sujeet Kumar Singh, Vinay Yadav
Summary: This article proposes an efficient scalarization technique to solve multi-objective optimization problems by introducing a modified goal function. The performance of the method is evaluated using closeness measure to the ideal solution and applied in finding the best locations for municipal solid waste management facilities. The study provides both theoretical advancement and a real-life application in MSW management.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Maghsoud Amiri, Mohammad Hashemi-Tabatabaei, Mohammad Ghahremanloo, Mehdi Keshavarz-Ghorabaee, Edmundas Kazimieras Zavadskas, Jurgita Antucheviciene
Summary: The study developed fuzzy linear programming models using trapezoidal fuzzy numbers for BWM in order to calculate optimal weights of criteria. The models are based on three measures of possibility, necessity, and credibility, allowing decision-makers to account for uncertainties and different attitudes. The results of the proposed approaches were tested with numerical examples and found to be robust, with the possibility approach providing more consistent results when decision-makers' levels of confidence change.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Energy & Fuels
Min Xiao, Ghassan Fadhil Smaisim
Summary: This paper introduces a method of using a multi-energy CHP-based microgrid and power-to-gas facility to address CO2 emissions. By using the joint chance constraint approach to evaluate uncertain parameters, the emission can be reduced while increasing the operation costs.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Chemical
Youzhi Wang, Zhong Li, Liu Liu, Ping Guo
Summary: The study develops a multi-objective stochastic programming model for irrigation water resources management under uncertainties. The model effectively deals with issues related to water demands, economic benefits, and water allocation in irrigation water resources management.
DESALINATION AND WATER TREATMENT
(2021)
Article
Engineering, Multidisciplinary
Md. Sharif Uddin, Musa Miah, Md. Al-Amin Khan, Ali AlArjani
Summary: This paper proposes a fuzzy membership function tactic based on goal programming to solve multi-objective transportation problems in uncertain environments, allowing decision makers to choose confidence levels for different parameters and determine feasibility and satisfaction levels through compromise solutions.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Management
Kevin-Martin Aigner, Jan-Patrick Clarner, Frauke Liers, Alexander Martin
Summary: This paper proposes a mathematical optimization model and its solution for joint chance constrained DC Optimal Power Flow. The proposed model minimizes curtailment of renewable energy feed-in while ensuring a high probability of transmission limits being maintained. The solution approach is based on robust safe approximation and replaces probabilistic constraints with suitably defined uncertainty sets constructed from historical data. Experimental results demonstrate the effectiveness and efficiency of this method.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Hui Wu, Qiong Yue, Ping Guo, Qi Pan, Shanshan Guo
Summary: This study establishes a water resources management modeling framework and optimizes system risks and resource consumption through multi-objective programming under uncertainty to achieve compromise resources consumption and environmental protection.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Automation & Control Systems
Defeng He, Haiping Li, Haiping Du
Summary: This paper introduces a new lexicographic multi-objective model predictive control scheme for handling constrained nonlinear systems with changing objective prioritization. The feasibility of lexicographic MoMPC in the presence of constraints and changing objective prioritization is restored by introducing a generic function and the notion of general terminal constraints.
Article
Transportation
Guangchao Wang, Kebo Tong, Anthony Chen, Hang Qi, Xiangdong Xu, Shoufeng Ma
Summary: This study investigates the impacts of the least perceived travel cost on the stochastic user equilibrium problem. The Weibit SUE models with a positive location parameter reduce perception variances route-specifically and resolve the scale insensitivity issue. Numerical results confirm the analytical results and demonstrate the efficiency and robustness of the proposed solution algorithm.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Management
Guoyuan Li, Anthony Chen
Summary: This paper proposes a strategy-based transit stochastic user equilibrium (SUE) model that considers capacity and number-of-transfers constraints in an urban congested transit network. The model uses a route-section-based method for network representation and assumes passengers' route choice behavior obeys the logit model. The transit line capacity and maximum number-of-transfers constraints are considered, and the problem is formulated as a variational inequality (VI) problem. A transit path-set generation procedure is proposed, and the asymmetric cost function is solved using the diagonalization method.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Transportation
Ruiya Chen, Xiangdong Xu, Anthony Chen, Xiaoning Zhang
Summary: This paper presents a conservative expected travel time approach, called MCET, for reporting reliable waiting time information in app-based transportation services, addressing the issues of existing information provision forms.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Transportation
Ruiya Chen, Xiangdong Xu, Anthony Chen, Chao Yang
Summary: Travel time variability poses challenges to reporting travel time information. This paper proposes a conservative expected travel time approach to enhance information reliability and simplicity.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Geography
Yingying Xu, Dawei Cheng, Ho-Yin Chan, Anthony Chen
Summary: Pedestrian infrastructures in Hong Kong are important in enabling multi-level city life in a land-scarce vertical metropolis, with public spaces integrated into pedestrian networks playing a crucial role in neighborhood accessibility. The impact of Covid-19 vaccine passport (VP) restrictions on the use of public space on pedestrian accessibility to all 97 metro stations in Hong Kong is visualized. Pedestrians without a vaccine passport (PwoVP) are required to take significantly longer alternative routes, with VP-related access restrictions to indoor walkways doubling the shortest travel time for PwoVP and reducing accessibility to two-thirds of the stations by 50%.
REGIONAL STUDIES REGIONAL SCIENCE
(2022)
Article
Engineering, Civil
Kaipeng Wang, Pu Wang, Zhiren Huang, Ximan Ling, Fan Zhang, Anthony Chen
Summary: In this study, a two-step model is developed to predict passenger travel demand in expanding subways and tested in an actual subway. Results show that the proposed model achieves higher prediction accuracy than the benchmark models.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Review
Transportation Science & Technology
Zhaoqi Zang, Xiangdong Xu, Kai Qu, Ruiya Chen, Anthony Chen
Summary: This paper introduces the importance of modeling travel time reliability (TTR) in transportation networks and provides an integrated framework for summarizing the methodological developments and applications of TTR. By adopting a network perspective, a better understanding of TTR characterization, evaluation and valuation, and traffic assignment can be achieved. The paper also discusses some common challenges in TTR modeling and potential directions for future research.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Economics
Yu Gu, Anthony Chen, Xiangdong Xu
Summary: This study proposes an optimization-based approach to rank the importance of link combinations and analyze network vulnerability in extreme and near-extreme cases of disruption. A vulnerability envelope concept is used, which considers the worst and best network performance under multiple-link disruptions. The results demonstrate that the consideration of near-extreme cases yields additional valuable information that is not generated by the traditional vulnerability analysis.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Transportation Science & Technology
Yu Gu, Anthony Chen
Summary: This study proposes an advanced equilibrium mode choice model to analyze the mode choice behavior of emerging customized bus (CB) services. The model considers the unique characteristics of CB services, including seat reservation and loyalty scheme. The results demonstrate the importance of considering passenger loyalty and managing mode similarity and heterogeneity when modeling emerging CB services.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Environmental Studies
Zhuowei Wang, Jiangbo Yu, Guoyuan Li, Chengxiang Zhuge, Anthony Chen
Summary: This study investigates the feasibility and policy implications of achieving carbon neutrality in Hong Kong's public transportation through a competitive bus-market mechanism. A dynamic bus-market evolution model is established using the system dynamics method, which incorporates a generalized Lotka-Volterra model and discrete choice model. The results suggest that relying on business-as-usual policies and market evolution may not be sufficient to achieve the desired level of zero-emission buses, and long-term subsidies for hydrogen buses and support for hydrogen stations are effective measures to promote the hydrogen bus market.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2023)
Article
Geography
Ho-Yin Chan, Yingying Xu, Anthony Chen, Xintao Liu, Kason Ka Ching Cheung
Summary: This article introduces a proof-of-concept designer-in-the-loop schematic map drawing tool, which combines manual and automated approaches to provide technical interactivity between the user and the computer. Compared to existing methods, the proposed approach is more compatible with the framework of effective map design from psychological and aesthetic perspectives, and offers a range of options based on user preferences.
TRANSACTIONS IN GIS
(2023)
Article
Environmental Studies
Shiqi Wang, Yuze Li, Anthony Chen, Chengxiang Zhuge
Summary: This paper develops a data-driven micro-simulation optimization model for deploying charging infrastructure for a large-scale electric bus network. The model considers both traditional charging posts and wireless charging lanes. The results show that deploying both charging posts and WCLs leads to higher levels of service, energy savings, and reduced emissions compared to deploying only charging posts, although the total costs are slightly higher. Sensitivity analysis confirms that parameters associated with electric buses and charging facilities significantly influence the model outputs.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2023)
Article
Economics
Zhandong Xu, Anthony Chen, Xiaobo Liu
Summary: This paper presents a continuous time surplus maximization bi-objective user equilibrium (C-TSmaxBUE) model, in which the users' variability toward the time and toll trade-off in a tolled road network is explicitly considered. The model assigns different users with different ratios of the time saved per unit of money (RTSMs), and infinite indifference curves are generated by considering continuously distributed RTSMs in the population. A path-based single-boundary adjustment (SBA) algorithm is developed to solve the problem, which adjusts RTSM boundaries and path flows simultaneously. Numerical results demonstrate the equilibrium flow pattern and the efficiency of the SBA algorithm in obtaining high-quality equilibrium solutions.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Economics
Umer Mansoor, Arshad Jamal, Junbiao Su, N. N. Sze, Anthony Chen
Summary: Motorcycle crashes cause a significant number of fatalities and severe injuries worldwide, especially in developing countries. Machine learning methods have been found to provide better prediction performance, but with weaker interpretability. This study aims to compare the consistency of risk factors identified by statistical models and machine learning methods in analyzing motorcycle crash severity.
Proceedings Paper
Computer Science, Artificial Intelligence
Muqing Du, Jiankun Zhou, Anthony Chen
Summary: In this study, a weibit-based SUE model was proposed to address the stochastic ridesharing user equilibrium problem. The model considers the conversion of travelers among three modes and the relationship between the number of ridesharing drivers and passengers, as well as a non-additive path cost function.
2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
(2022)
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)
