Article
Mathematics
Arijit Ghosh, Neha Ghorui, Sankar Prasad Mondal, Suchitra Kumari, Biraj Kanti Mondal, Aditya Das, Mahananda Sen Gupta
Summary: This paper explores the application of hexagonal fuzzy multiple-criteria decision-making (MCDM) methodology for the site selection of electric vehicle charging stations. By integrating GIS with MCDM techniques, fuzzy analytic hierarchy process (FAHP) is used to obtain fuzzy weights, while fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) and fuzzy complex proportional assessment (FCOPRAS) are utilized for ranking selected sites. The study also introduces a centroid-based defuzzification method and a measure for distance between two hexagonal fuzzy numbers (HFN) for this purpose.
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.
Article
Computer Science, Interdisciplinary Applications
Guilherme Henrique de Paula Vidal, Rodrigo Goyannes Gusmao Caiado, Luiz Felipe Scavarda, Paulo Ivson, Jose Arturo Garza-Reyes
Summary: This study proposes a decision support framework for inventory management, combining multicriteria decision-making (MCDM) and machine learning (ML) approaches. It applies the framework to a railway logistics operator to assist its MRO inventory management decision-making process. The research findings show a considerable improvement in the accuracy of SKU demand forecast and the overall decision-making process in inventory management.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Mathematics
Svajone Bekesiene, Oleksandr Nakonechnyi, Olena Kapustyan, Rasa Smaliukiene, Ramute Vaicaitiene, Dalia Bagdziuniene, Rosita Kanapeckaite
Summary: This study utilized a fuzzy MCDM method to establish the priority weight of decision-making criteria and identify core competencies for soldier resilience training. The study also used the fuzzy TOPSIS method to rank and select the most appropriate training program. The results indicated that mental agility is the most important competence in high-stress environments, and the Mindfulness-Based Mind Fitness Training (MMFT) program ranked the highest among the evaluated options.
Article
Operations Research & Management Science
Kamal Hossain Gazi, Sankar Prasad Mondal, Banashree Chatterjee, Neha Ghorui, Arijit Ghosh, Debashis De
Summary: This research addresses the problem of ranking restaurant locations in the cosmopolitan city of Kolkata, India. It considers various factors and sub-factors to select and rank restaurants in the city. Hexagonal fuzzy numbers are used to deal with the uncertainty, and analytic hierarchy process (AHP) is employed as a multi-criteria decision-making tool. The TOPSIS and COPRAS methods resulted in better ranking compared to other fuzzy numbers, and the sensitivity analysis provides guidance for decision-making in different scenarios.
RAIRO-OPERATIONS RESEARCH
(2023)
Article
Mathematics
Jui-Fang Chang, Chao-Jung Lai, Chia-Nan Wang, Ming-Hsien Hsueh, Van Thanh Nguyen
Summary: Choosing a supplier is a complex decision-making process that can be supported by using multicriteria decision-making models. This paper presents an integrated mathematical model under a fuzzy environment and applies it to the supplier selection process in the garment industry. The study results show that supplier 08 is the optimal supplier, and this approach can also be applied to support complex decision-making processes in different industries.
Article
Engineering, Geological
Ming -Yang Xu, Da-Gang Lu, Xiao-Hui Yu, Ming -Ming Jia
Summary: This paper proposes a fuzzy decision-theoretic computational approach for selecting optimal intensity measures (IMs) under the conditions of randomness, fuzziness, and uncertainty. The study aims to quantitatively evaluate multiple evaluation criteria and improve the credibility of decision-making results by selecting the appropriate multi-criteria decision making (MCDM) method. The approach utilizes fuzzy-probabilistic seismic de-mand analysis (FPSDA), fuzzy analytical hierarchical process (FAHP), and a combination of FAHP and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to determine the optimal IM alternatives.
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
(2023)
Article
Mathematics
Svajone Bekesiene, Aidas Vasilis Vasiliauskas, Sarka Hoskova-Mayerova, Virgilija Vasiliene-Vasiliauskiene
Summary: This paper presents the application of Fuzzy AHP-TOPSIS hybrid method in distance learning quality assessment surveys. Thirty-four judges with specific knowledge and skills were chosen to evaluate three alternatives by fourteen criteria, and statistical analysis was used to process the data. The study further provides useful guidelines for the development of an easily understandable hierarchy of criteria model reflecting the main goal of study quality assessment.
Article
Computer Science, Information Systems
Chia-Nan Wang, Chien-Chang Chou, Hsien-Pin Hsu, Van Thanh Nguyen, Viet Tinh Nguyen
Summary: During the COVID-19 pandemic, selecting the location of temporary hospitals and prioritizing preventive measures are critical decisions. This study proposes a decision support framework using FAHP and a weighted aggregated sum product assessment model for location selection, as well as a FAHP model for prioritizing preventive measures. A case study in Ho Chi Minh City validates the usefulness of this framework.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Information Systems
Chia-Nan Wang, Chao-Fen Pan, Viet Tinh Nguyen, Syed Tam Husain
Summary: As global supply chains become more advanced and complicated, supplier quality plays an increasingly important role in business competitiveness during the Covid-19 pandemic. Supplier selection is crucial for any global business, as it can result in lower procurement costs and increased profits without compromising product quality. However, decision-making problems can be complex when there are multiple potential suppliers. This paper introduces a hybrid MCDM model using the FAHP and TOPSIS methods in a fuzzy decision-making environment to assist the supplier selection process in the garment industry.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Information Systems
Anurag Sharma, Ripon K. Chakrabortty, Vikrant Sharma, Hitesh Marwaha, Parulpreet Singh, Shubham Mahajan, Amit Kant Pandit
Summary: The research aims to develop a tool for screening Asperger Syndrome (AS) using a multi-criteria decision-making (MCDM) method and fuzzy logic to handle ambiguity. The fuzzy analytic hierarchy process (FAHP) algorithm is used with the If-Then rule-based approach to determine if an individual has AS. The developed tool, named Fuzzy Tree, reduces the number of rules and provides accurate results within a short time, with a precision of 99% confirmed by validation experiments involving AS and Typically Developed (TD) groups.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Melike Erdogan, Ihsan Kaya, Ali Karasan, Murat Colak
Summary: This paper proposes a study using a multi-criteria decision-making method to evaluate alternative solutions of autonomous vehicle driving systems, taking into account risk criteria. The results of the study indicate that "Software Specifications" and "Reliability" are the most important main and sub-criteria.
APPLIED SOFT COMPUTING
(2021)
Article
Multidisciplinary Sciences
Huseyin Kamaci, Dragan Marinkovic, Subramanian Petchimuthu, Muhammad Riaz, Shahzaib Ashra
Summary: This paper proposes a novel concept called linguistic linear Diophantine fuzzy set to handle linguistic uncertainty in real-world decision support problems. By extending the restrictions on the grades, this method allows for more flexibility in evaluating and expressing judgments about membership and nonmembership. Through analysis and comparison, the proposed method demonstrates advantages over existing decision support techniques.
Article
Green & Sustainable Science & Technology
Sahand Somi, Nima Gerami Seresht, Aminah Robinson Fayek
Summary: This paper explores the risks associated with onshore wind farm construction projects, and develops a new risk identification technique based on case-based reasoning and fuzzy logic to enhance the risk management process for these projects.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Mathematics, Applied
Chia-Nan Wang, Thanh-Tuan Dang, Ngoc-Ai-Thy Nguyen
Summary: This study aims to provide a more complete and robust evaluation process to e-commerce businesses and any organization that deals with supply chain management in determining the optimized reverse logistics partners by using a hybrid fuzzy multicriteria decision-making approach.
Article
Automation & Control Systems
J. M. Sanchez-Lozano, M. Fernandez-Martinez, M. T. Lamata
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2019)
Article
Chemistry, Multidisciplinary
Alvaro Rubio-Aliaga, Angel Molina-Garcia, M. Socorro Garcia-Cascales, Juan Miguel Sanchez-Lozano
APPLIED SCIENCES-BASEL
(2019)
Article
Green & Sustainable Science & Technology
A. Rubio-Aliaga, M. S. Garcia-Cascales, J. M. Sanchez-Lozano, A. Molina-Garcia
Article
Computer Science, Artificial Intelligence
J. M. Sanchez-Lozano, O. Naranjo Rodriguez
APPLIED SOFT COMPUTING
(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
Green & Sustainable Science & Technology
J. M. Sanchez-Lozano, F. J. Salmeron-Vera, C. Ros-Casajus
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)
Proceedings Paper
Engineering, Electrical & Electronic
M. Socorro Garcia-Cascales, Angel Molina-Garcia, Juan Miguel Sanchez-Lozano, Alvaro Rubio-Aliaga, Nolberto Munier
2020 20TH IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2020 4TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE)
(2020)
Article
Engineering, Aerospace
J. M. Sanchez-Lozano, M. Fernandez-Martinez, A. A. Saucedo-Fernandez, Josep M. Trigo-Rodriguez
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.
