Article
Engineering, Multidisciplinary
M. A. Elsisy, M. A. El Sayed, Y. Abo-Elnaga
Summary: This paper introduces a new algorithm for generating the Pareto frontier for a bi-level multi-objective rough nonlinear programming problem by transforming it into four deterministic models and using a combination of the weighted method and KKT optimality condition to obtain the Pareto front of each model. The proposed solution aims to avoid solving four problems and is demonstrated through a numerical example.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Computer Science, Information Systems
Seiki Ubukata, Akira Notsu, Katsuhiro Honda
Summary: Hard C-means (HCM) has been extended to rough C-means (RCM) to handle the certain, possible, and uncertain belonging of objects to clusters. Furthermore, rough set C-means (RSCM) and rough membership C-means (RMCM) have been proposed as clustering models based on binary relations in an approximation space. A novel RMCM framework, RMCM version 2 (RMCM2), has been proposed in this paper, which is based on an objective function and demonstrates characteristics through visualizing cluster boundaries and verifying clustering performance with real-world datasets.
INFORMATION SCIENCES
(2021)
Article
Thermodynamics
Lucas F. Santos, Caliane B. B. Costa, Jose A. Caballero, Mauro A. S. S. Ravagnani
Summary: A surrogate-based multi-objective optimization framework is used to design natural gas liquefaction processes and compare their energy consumption and heat exchanger area utilization. The surrogate-based framework provides better solutions than traditional multi-objective optimization methods. The results contribute to achieving lower energy consumption and higher heat exchanger utilization efficiency.
Article
Computer Science, Artificial Intelligence
Harish Garg, Sultan S. Alodhaibi, Hamiden Abd El-Wahed Khalifa
Summary: This paper investigates the multi-objective nonlinear programming problem with rough parameters. The problem is transformed into lower and upper approximate problems, and efficient solutions are obtained using the Karush-Kuhn-Tucker optimality conditions. The rough weights and parameters are also determined.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Ieva Meidute-Kavaliauskiene, Vida Davidaviciene, Shahryar Ghorbani, Iman Ghasemian Sahebi
Summary: The study focuses on the optimal allocation of natural gas resources to various consumption sectors in Iran by prioritizing the energy security index using a multi-objective goal programming model. The analysis reveals that household business, power plants, petrochemical industries, industry, and export aid injection to oil fields are the most consuming sectors in 2025. The research also suggests that power plants, petrochemical industries, and industries in general are the more consuming sectors based on cost minimization.
Article
Green & Sustainable Science & Technology
Teg Alam
Summary: This research introduces a preemptive goal-programming model to optimize sustainable multi-objective production planning in the refrigeration and air conditioning industry of Saudi Arabia. Using LINGO software, the model considers market demand, production revenue, production time, and production cost data. The findings demonstrate successful achievement of minimizing production cost, maximizing sales revenue, and maximizing machine utilization without any deviation. Sensitivity analysis suggests a 2.14% increase in costs to minimize production cost, but this could reduce revenues by 4.37%. Overall, the goal-programming model showcases the potential of the Saudi Arabian refrigeration and air conditioning industry in achieving cost optimization, sales revenue maximization, and resource utilization.
Article
Computer Science, Interdisciplinary Applications
Jin Ye, Bingzhen Sun, Qiang Bao, Chun Che, Qingchun Huang, Xiaoli Chu
Summary: Based on Pythagorean fuzzy sets and rough set theory, this paper proposes a novel decision-making method for multi-objective problems with fuzziness and uncertainty. Through case studies on clinical treatment scheme selection, the effectiveness and superiority of the method are demonstrated through comparative analysis. The work of this paper is of great significance for solving complex multi-objective decision-making problems.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Operations Research & Management Science
Prerna Kushwah, Vikas Sharma
Summary: This paper discusses an algorithm for solving multi-objective quadratic programming problems with integer variables. The algorithm can find all efficient solutions and has high efficiency in solving large-scale problems.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Chemistry, Physical
Zihang Zhang, Isam Saedi, Sleiman Mhanna, Kai Wu, Pierluigi Mancarella
Summary: The study presents a transient analysis model for tracking gas compositions and hydrogen fractions in meshed networks with multiple NG and intermittent hydrogen sources. By introducing a time-varying compressibility factor and numerical techniques, the method effectively addresses the decarbonization challenges in natural gas systems with hydrogen blending.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Mathematics, Interdisciplinary Applications
Mohamed A. El Sayed, Mohamed A. El-Shorbagy, Farahat A. Farahat, Aisha F. Fareed, Mohamed A. Elsisy
Summary: This study introduces a parametric intuitionistic fuzzy multi-objective fractional transportation problem, utilizes a fuzzy goal programming approach to obtain Pareto optimal solution, and investigates the stability set of the first kind corresponding to the solution.
FRACTAL AND FRACTIONAL
(2021)
Article
Operations Research & Management Science
Sudipta Midya, Sankar Kumar Roy, Gerhard Wilhelm Weber
Summary: This article presents a multiple objective fractional fixed-charge transportation problem in a rough decision-making framework, where the fuzzy chance-constrained rough approximation technique is employed to extract the optimal solution. The article deals with fuzzy parameters using different types of fuzzy scales, and expands the feasible domain of the problem by considering rough sets theory. The optimal solutions belong to two separate regions, surely region and possible region, with the surely region providing better minimum values.
RAIRO-OPERATIONS RESEARCH
(2021)
Article
Energy & Fuels
Lucas F. Santos, Caliane B. B. Costa, Jose A. Caballero, Mauro A. S. S. Ravagnani
Summary: This study proposes a framework for optimizing the energy-efficient design of natural gas liquefaction process using algebraic surrogate models and efficient optimization methods, showing that multi-stage expansion can significantly increase energy savings.
Article
Engineering, Chemical
Chung-Fu Huang, Wei-Ting Chen, Chuan-Ksing Kao, Han-Jung Chang, Po-Min Kao, Terng-Jou Wan
Summary: Planning of sewer systems involves limitations and problems regardless of the methods used. Uncertainties and non-quantifiability affect decision-making variables and the optimal solution. This study used multi-objective programming, nonlinear programming, mixed-integer programming, and compromise fuzzy programming to address these problems for regional sewer system planning. The study aimed to determine necessary variables and establish a framework for optimal planning that considers cost, space, energy requirements, consumption, and treatment efficiency. The findings showed that merging individual sewage treatment plants into a centralized system was possible, and the compromise-fuzzy-based MOP method was effective in optimizing the system plan.
Article
Computer Science, Artificial Intelligence
Patrick Doherty, Andrzej Szalas
Summary: This paper discusses methods for dealing with uncertainty, introduces a reasoning tool based on Answer Set Programming, and provides a technique for handling incomplete information systems. Rough set theory is a modeling method for dealing with incompleteness and imprecision.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
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)
Review
Management
Desheng Wu, David L. Olson, Alexandre Dolgui
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2015)
Article
Automation & Control Systems
Desheng Dash Wu
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2015)
Article
Computer Science, Information Systems
Desheng Dash Wu, Shu-Heng Chen, David L. Olson
INFORMATION SCIENCES
(2014)
Article
Engineering, Industrial
Fuguo Zhao, Desheng Dash Wu, Liang Liang, Alexandre Dolgui
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2015)
Article
Computer Science, Artificial Intelligence
Chunqiao Tan, Benjiang Ma, Desheng Dash Wu, Xiaohong Chen
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
(2014)
Article
Management
D. Wu, C. Luo, L. Liang, A. Dolgui
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2014)
Article
Computer Science, Interdisciplinary Applications
David L. Olson, Desheng Wu
MATHEMATICAL AND COMPUTER MODELLING
(2013)
Article
Computer Science, Interdisciplinary Applications
Desheng Dash Wu
MATHEMATICAL AND COMPUTER MODELLING
(2013)
Editorial Material
Computer Science, Interdisciplinary Applications
Desheng Dash Wu, David L. Olson
MATHEMATICAL AND COMPUTER MODELLING
(2013)
Article
Automation & Control Systems
Desheng Dash Wu, Lijuan Zheng, David L. Olson
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2014)
Article
Automation & Control Systems
Desheng Dash Wu, Cuicui Luo, David L. Olson
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2014)
Article
Automation & Control Systems
Desheng D. Wu, David L. Olson, Cuicui Luo
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2014)
Article
Management
Desheng Dash Wu, Jia Liu, David L. Olson
SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE
(2015)
Article
Management
Desheng Dash Wu, David L. Olson
SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE
(2014)
Article
Business
You Daming, Yang Xiaohui, Desheng Dash Wu, Chen Guofan
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2014)
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)