Article
Multidisciplinary Sciences
Guangying Jin, Guangzhe Jin
Summary: This paper utilizes MCDM methods for the first time to analyze the selection of fault-diagnosis sensors for Fuel Cell Stack systems, proposing a methodology that combines AHP and TOPSIS to support companies in choosing the optimal sensor solution. The analysis shows that the optimal solution can be mapped to the TOPSIS method to solve the optimization problem, making the fault-diagnosis process of the system more efficient, economical, and safe.
Article
Materials Science, Composites
Ramkumar Yadav
Summary: This study investigates the optimal selection of dental composites using AHP and TOPSIS, which reveals that the AHP-TOPSIS method is an effective MCDM strategy for ranking under different performance defining criteria.
POLYMER COMPOSITES
(2021)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Article
Chemistry, Multidisciplinary
Ghasan Alfalah, Munther Al-Shalwi, Nehal Elshaboury, Abobakr Al-Sakkaf, Othman Alshamrani, Altyeb Qassim
Summary: Fires pose significant risks to life, property, and the economy. Traditional fire safety assessment methods are laborious and challenging, hindering the identification of fire hazards and optimal safety measures. This research introduces an analytic hierarchy process for assessing building fire safety, supported by case studies that demonstrate its superiority over conventional techniques. The proposed method provides hazard and safety ratings, yielding comprehensible and comparable results, making it a valuable decision-making tool for enhancing fire safety in buildings.
APPLIED SCIENCES-BASEL
(2023)
Review
Mathematics
Sangeeta Pant, Anuj Kumar, Mangey Ram, Yury Klochkov, Hitesh Kumar Sharma
Summary: This article provides a brief review of the consistency measures in AHP and the functional relationships among different consistency indices. It also offers some thoughtful research directions for further development and improvement of AHP.
Article
Public, Environmental & Occupational Health
Shiqian Wang, Lin Li, Yanjun Jin, Rui Liao, Yen-Ching Chuang, Zhong Zhu
Summary: A model was developed to evaluate and identify key factors contributing to burnout in orthopedic surgeons, providing guidance for managing burnout in hospitals. The model used the analytic hierarchy process (AHP) with 3 dimensions and 10 sub-criteria based on literature review and expert assessment. Expert and purposive sampling was conducted, and 17 orthopedic surgeons were selected as research subjects. The results showed that the personal/family dimension was the key factor affecting burnout, with the top four sub-criteria being little time for family, anxiety about clinical competence, work-family conflict, and heavy work load.
INTERNATIONAL JOURNAL OF PUBLIC HEALTH
(2023)
Article
Mathematics
Chin-Yi Chen, Jih-Jeng Huang
Summary: This paper presents an innovative method that integrates dynamic Bayesian networks (DBNs) with the analytic hierarchy process (AHP) to model dynamic interdependencies between criteria in multi-criteria decision-making (MCDM) problems. The proposed method extends the AHP to accommodate time-dependent issues and reduces to the conventional AHP when ignoring specific information, making it a more general AHP model.
Article
Engineering, Marine
H. Diaz, A. P. Teixeira, C. Guedes Soares
Summary: A Monte Carlo simulation procedure is developed to select the optimum location of wind farms by combining major decision criteria and subjective judgments from decision-makers. The method utilizes Monte Carlo simulation, conventional Analytic Hierarchy Process, and Fuzzy Analytic Hierarchy Process. It is applied to offshore wind farms in Spain to rank the most suitable turbine positioning locations.
Review
Computer Science, Information Systems
Sarbast Moslem, Mahyar Kamali Saraji, Abbas Mardani, Ahmad Alkharabsheh, Szabolcs Duleba, Domokos Esztergar-Kiss
Summary: The current study provides a thorough review of transportation problems using the Analytic Hierarchic Process (AHP) and analyzes 58 papers published from 2003 to 2019. The results show that most researchers applied the conventional AHP method to address transportation issues, with public transport being the most critical problem, followed by logistics problems. AHP was commonly integrated with TOPSIS when dealing with multi-criteria transportation problems. The study contributes by extending the use of AHP in decision-making support and positively impacting technological and socio-economic development in the transportation sector.
Article
Thermodynamics
Abderrahmane Gouareh, Belkhir Settou, Noureddine Settou
Summary: This study presents an alternative framework for site selection of large-scale parabolic trough concentrating solar power plants in Algeria using GIS analysis and Multi Criteria Decision Making techniques. The research assesses the theoretical and technical potential of energy generation using concentrating solar power plants, showing that approximately 11% of the study area is suitable for energy generation with a yearly electricity generation of 34,453 TWh.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Phi-Hung Nguyen
Summary: The outbreak of COVID-19 has had a significant impact on the global economy, particularly on the agricultural supply chains in developing and third world countries. This study assessed the critical risks associated with these supply chains and identified transportation, market, and policy as the most important factors in mitigating the risks.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Materials Science, Textiles
Ashis Mitra, Abhijit Majumdar
Summary: The integration of the newly developed Best-Worst Method (BWM) with Revised Analytic Hierarchy Process (RAHP) provides a reliable approach to rank cotton fiber lots based on their quality value. This vector-based BWM approach, which requires fewer pairwise comparisons, is applied for the first time in a cotton fiber grading problem. The stability and robustness of the BWM-RAHP method are confirmed by the absence of rank reversal during sensitivity analyses, and the rank correlations between the quality value ranking and yarn tenacity ranking further support its efficacy.
JOURNAL OF NATURAL FIBERS
(2023)
Article
Mathematics
Anna Kedzior, Konrad Kulakowski
Summary: The Analytic Hierarchy Process (AHP) is a widely used multi-criteria decision-making method that combines pairwise comparisons and hierarchical approach. It allows decision-makers to prioritize ranked alternatives. The Heuristic Rating Estimation (HRE) method proposed in 2014 aims to address cases where ranking values for some alternatives are known differently. However, the previous study only considered a model with a few criteria. This work analyzes the integration of HRE into the AHP hierarchical framework and provides illustrative examples of its application as a multiple-criteria decision-making method.
Article
Environmental Sciences
Li Zhu, Zhonghua Zhao, Yiping Wang, Qunwu Huang, Yong Sun, Dapeng Bi
Summary: This study utilized Analytic Hierarchy Process and Life Cycle Assessment methods to determine evaluation indicators and rankings, proposing a Toilet Assessment Scheme. The results indicated that water conservation, environmental protection, and indoor environmental quality should be prioritized. Additionally, experts had differing emphases on the evaluation scheme based on gender, profession, and generation.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Fisheries
Nishan Raja Raja, Nila Rekha Peter, Sandeep Kizhakkekarammal Puthiyedathu, Chandrasekar Vasudevan, Soumyabrata Sarkar, Albin Sunny
Summary: Cage farming is becoming popular in brackishwater resources in India, and this study aims to locate suitable cage farming sites in the Muttukadu brackishwater ecosystem. By considering water quality, environmental factors, and accessibility, the study used the fuzzy analytic hierarchy process to identify potential areas for cage farming development.
AQUACULTURE INTERNATIONAL
(2023)
Article
Computer Science, Artificial Intelligence
J. M. Sanchez-Lozano, O. Naranjo Rodriguez
APPLIED SOFT COMPUTING
(2020)
Article
Chemistry, Multidisciplinary
Y. Guerrero-Sanchez, M. Fernandez-Martinez, P. Lopez-Jornet, F. J. Gomez-Garcia
JOURNAL OF MATHEMATICAL CHEMISTRY
(2020)
Article
Mathematics, Interdisciplinary Applications
M. Fernandez-Martinez, M. A. Sanchez-Granero, M. P. Casado Belmonte, J. E. Trinidad Segovia
CHAOS SOLITONS & FRACTALS
(2020)
Article
Green & Sustainable Science & Technology
Alvaro Rubio-Aliaga, M. Socorro Garcia-Cascales, Juan Miguel Sanchez-Lozano, Angel Molina-Garcia
Summary: This study estimates and classifies optimal groundwater pumping solutions based on various energy resources and water storage options. A multi-criteria decision making process is applied using AHP and TOPSIS methods to identify and prioritize the solutions. The study focuses on a case study in southeast Spain and discusses potential energy and water storage options.
Article
Mathematics
M. Fernandez-Martinez, Juan L. G. Guirao
Article
Engineering, Aerospace
Michael C. F. Bazzocchi, Juan Miguel Sanchez-Lozano, Houman Hakima
Summary: This study presents an approach for prioritizing space debris through Multi-Criteria Decision-Making methodologies and fuzzy logic, considering various criteria such as orbit, size, collision probability, etc. The method involves assigning attributes, preparing a questionnaire for experts, examining over two thousand critical debris objects, and using fuzzy versions of Analytic Hierarchy Process and Topsis for aggregation of quantified attributes to identify high-priority debris objects for removal.
ADVANCES IN SPACE RESEARCH
(2021)
Article
Astronomy & Astrophysics
M. Fernandez-Martinez, J. M. Sanchez-Lozano
Summary: The extension of fuzzy sets to broader contexts is a leading area of research in artificial intelligence, aiming to address decision-making problems in the real world where obtaining accurate data is difficult. Recent introduction of spherical fuzzy sets allows for more precise modeling of problems based on human nature, expanding membership levels under imprecise circumstances. The study applies the spherical fuzzy set version of TOPSIS in the context of planetary defense, showing that kinetic impactors are the most suitable alternative and the spherical fuzzy set version of TOPSIS is more sensitive to expert information compared to triangular fuzzy sets.
ADVANCES IN ASTRONOMY
(2021)
Article
Astronomy & Astrophysics
J. M. Sanchez-Lozano, A. Moya, J. M. Rodriguez-Mozos
Summary: This study utilized multiple-criteria decision-making methodologies and fuzzy logic to analyze the habitability potential of 1798 exoplanets. The results indicated that Kepler-442b, Kepler-062e/f, and LHS_1140b are the best candidates for searching for biomarkers, while TRAPPIST-1e is the most feasible candidate considering technical limitations.
ASTRONOMY AND COMPUTING
(2021)
Article
Engineering, Environmental
Carmen Fernandez-Lopez, Mariano Gonzalez Garcia, Juan Miguel Sanchez-Lozano
Summary: A fuzzy version of a Multi-Criteria Decision Making (MCDM) method called TOPSIS is developed to help in the decisions to design WWTPs when the efficiency of PhAC removal must be considered. Through the study of eleven alternatives (WWTPs) located in the Southeast of Spain, it was found that the most efficient WWTPs in the removal of each PhAC can be classified, indicating that technology standard is not the most important factor.
JOURNAL OF WATER PROCESS ENGINEERING
(2021)
Article
Energy & Fuels
M. S. Garcia-Cascales, A. Molina-Garcia, J. M. Sanchez-Lozano, A. Mateo-Aroca, N. Munier
Summary: This paper focuses on the main challenge of groundwater pumping solutions in reducing fossil fuel dependence and integrating renewables, while also considering sustainability criteria. Through analyzing and comparing different Multi-Criteria Decision Analysis methods, the relevance of subjective criteria weights is highlighted.
Article
Computer Science, Artificial Intelligence
Juan Miguel Sanchez-Lozano, Adela Ramos-Escudero, Isabel C. Gil-Garcia, Ma Socorro Garcia-Cascales, Angel Molina-Garcia
Summary: The research compares various fuzzy MCDM methodologies for selecting optimal locations of offshore wind power plants, showing the robustness of fuzzy TOPSIS method and the minimal impact of different fuzzy membership functions on the fuzzy VIKOR method.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
J. M. Sanchez-Lozano, J. C. Correa-Rubio, M. Fernandez-Martinez
Summary: This paper presents the first study on international military high-performance aircrafts and their defense weaponry systems, using fuzzy logic for analysis. The results indicate that the F-16 jet and the SCALP EG missile are the best choices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Aerospace
J. M. Sanchez-Lozano, M. Fernandez-Martinez, A. A. Saucedo-Fernandez, Josep M. Trigo-Rodriguez
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
