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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

Computer Modeling in Engineering & Sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar dragan pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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A Stable Fuzzy-Based Computational Model and Control for Inductions Motors

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工程与科学中的计算机建模(英文) 2024年第1期138卷 793-812页

作者： Yongqiu Liu Shaohui Zhong Nasreen Kausar Chunwei Zhang Ardashir Mohammadzadeh dragan pamucar School of Mechanical and Electrical Engineering Guangdong University of Science&TechnologyDongguan523083China School of Information Hunan Open UniversityChangsha410081China Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34220Turkey Multidisciplinary Center for Infrastructure Engineering Shenyang University of TechnologyShenyang110870China Faculty of Organizational Sciences University of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan320315TaiwanChina

In this paper,a stable and adaptive sliding mode control(SMC)method for induction motors is introduced.Determining the parameters of this system has been one of the existing challenges.To solve this challenge,a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism.According to the dynamic changes of the system,in addition to the parameters of the SMC,the parameters of the type-2 fuzzy neural network are also updated online.The conditions for guaranteeing the convergence and stability of the control system are provided.In the simulation part,in order to test the proposed method,several uncertain models and load torque have been applied.Also,the results have been compared to the SMC based on the type-1 fuzzy system,the traditional SMC,and the PI controller.The average RMSE in different scenarios,for type-2 fuzzy SMC,is 0.0311,for type-1 fuzzy SMC is 0.0497,for traditional SMC is 0.0778,and finally for PI controller is 0.0997.

关键词： Sliding mode control self-tuning type-2 fuzzy systems inductions motor parameters uncertainty

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A modified EDAS model for comparison of mobile wallet service providers in India

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Financial Innovation 2023年第1期9卷 1215-1245页

作者： Sanjib Biswas dragan pamucar Decision Sciences and Operations Management Calcutta Business SchoolBishnupurSouth 24 ParganasWest Bengal 743503India Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of Belgrade11000 BelgradeSerbia

The present paper has two-fold purposes.First,the current work provides an integrated theoretical framework to compare popular mobile wallet service providers based on users’views in the Indian context.To this end,we propose a new grey correlationbased Picture Fuzzy-Evaluation based on Distance from Average Solution(GCPF-EDAS)framework for the comparative analysis.We integrate the fundamental framework of the Technology Acceptance Model and Unified theory of acceptance and use of technology vis-a-vis service quality dimensions for criteria selection.For comparative ranking,we conduct our analysis under uncertain environments using picture fuzzy numbers.We find that user-friendliness,a wide variety of use,and familiarity and awareness about the products help reduce the uncertainty factors and obtain positive impressions from the users.It is seen that PhonePe(A3),Google Pay(A2),Amazon Pay(A4)and PayTM(A1)hold top positions.For validation of the result,we first compare the ranking provided by our proposed model with that derived by using picture fuzzy score based extensions of EDAS and another widely used algorithm such as The Technique for Order of Preference by Similarity to Ideal Solution.We observe a significant consistency.We then carry out rank reversal test for GCPF-EDAS model.We notice that our proposed GCPF-EDAS model does not suffers from rank reversal phenomenon.To examine the stability in the result for further validation,we carry out the sensitivity analysis by varying the differentiating coefficient and exchanging the criteria weights.We find that our proposed method provides stable result for the present case study and performs better as ranking order does not get changed significantly with the changes in the given conditions.

关键词： Picture fuzzy Grey correlation EDAS method TOPSIS Comparison of mobile wallets

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Hybrid q-Rung Orthopair Fuzzy Sets Based CoCoSo Model for Floating Offshore Wind Farm Site Selection in Norway

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CSEE Journal of Power and Energy Systems 2022年第5期8卷 1261-1280页

作者： Muhammet Deveci dragan pamucar Umit Cali Emre Kantar Konstanze Kolle John O.Tande Department of Industrial Engineering Turkish Naval AcademyNational Defence UniversityIstanbul 34940Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgradeSerbia Department of Electric Power Engineering Norwegian University of Science and TechnologyTrondheim NO-7491Norway Department of Electric Power Technology SINTEF Energy ResearchTrondheim NO-7491Norway Department of Energy Systems SINTEF Energy ResearchTrondheim NO-7034Norway

Unlocking offshore wind farms’high energy generation potential requires a comprehensive multi-disciplinary analysis that consists of intensive technical,economic,logistical,and environmental investigations.Offshore wind energy projects have high investment volumes that make it essential to conduct extensive site selection to ensure feasible investment decisions that reduce the potential financial risks.Depending on the scenario and circumstances,a ranking of alternative offshore wind energy projects helps to prioritise the investment decisions.Decisionmaking algorithms based on expert knowledge can support the prioritisation and thus alleviate the work load for investment decisions in the future.The case study considered here is to find the best site for a floating offshore wind farm in Norway from four pre-selected alternatives:Utsira Nord,Stadthavet,Froyabanken,and Trana Vest.We propose a hybrid decisionmaking model as a combined compromised solution(CoCoSo)based on the q-rung orthopair fuzzy sets(q-ROFSs)including the weighted q-rung orthopair fuzzy Hamacher average(Wq-ROFHA)and the weighted q-rung orthopair fuzzy Hamacher geometric mean(Wq-ROFHGM)operators.In this model,the q-ROFSs based full consistency method(FUCOM)is introduced as a new methodology to determine the weights of the decision criteria.The results of the proposed model show that the best site among the investigated four alternatives is A1:Utsira Nord.A sensitivity analysis has verified the stability of the proposed decision-making model.

关键词： Decision-making q-rung orthopair fuzzy sets Fuzzy hamacher site selection offshore wind farm FUCOM

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Multimodal Fuzzy Downstream Petroleum Supply Chain:A Novel Pentagonal Fuzzy Optimization

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Computers, Materials & Continua 2023年第3期74卷 4861-4879页

作者： Gul Freen Sajida Kousar Nasreen Kausar dragan pamucar Georgia Irina Oros Department of Mathematics and Statistics International Islamic UniversityIslamabadPakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Logistics University of Defence in BelgradeBelgradeSerbia Department of Mathematics and Computer Science University of OradeaOradea410087Romania

The petroleum industry has a complex,inflexible and challenging supply chain(SC)that impacts both the national economy as well as people’s daily lives with a range of services,including transportation,heating,electricity,lubricants,as well as chemicals and petrochemicals.In the petroleum industry,supply chain management presents several challenges,especially in the logistics sector,that are not found in other industries.In addition,logistical challenges contribute significantly to the cost of oil.Uncertainty regarding customer demand and supply significantly affects SC networks.Hence,SC flexibility can be maintained by addressing uncertainty.On the other hand,in the real world,decision-making challenges are often ambiguous or vague.In some cases,measurements are incorrect owing to measurement errors,instrument faults,etc.,which lead to a pentagonal fuzzy number(PFN)which is the extension of a fuzzy number.Therefore,it is necessary to develop quantitative models to optimize logistics operations and supply chain networks.This study proposed a linear programming model under an uncertain environment.The model minimizes the cost along the refineries,depots,multimode transport and demand nodes.Further developed pentagonal fuzzy optimization,an alternative approach is developed to solve the downstream supply chain using themixed-integer linear programming(MILP)model to obtain a feasible solution to the fuzzy transportation cost problem.In this model,the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number.Furthermore,defuzzification is performed using an accuracy function.To validate the model and technique and feasibility solution,an illustrative example of the oil and gas SC is considered,providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.

关键词： Downstream petroleum supply chain fuzzy optimization multimodal optimization pentagonal fuzzy number

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Aggregation Operators for Decision Making Based on q-Rung Orthopair Fuzzy Hypersoft Sets:An Application in Real Estate Project

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 3141-3156页

作者： Salma Khan Muhammad Gulistan Nasreen Kausar dragan pamucar Tzung-Pei Hong Hafiz Abdul Wahab Department of Mathematics and Statistics Hazara University Mansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Faculty of Organizational Sciences University of BelgradeBelgrade11000Serbia Department of Computer Science and Information Engineering National University of KaohsiungKaohsiung804Taiwan Department of Computer Science and Engineering National Sun Yat-sen UniversityKaohsiung804Taiwan

In this paper,a decision-making problem with a q-rung orthopair fuzzy hypersoft environment is developed,and two operators of ordered weighted average and induced ordered weighted average are developed.Several fundamental features are also derived.The induced ordered weighted average operator is essential in a q-ROFH environment as the induced ordered aggregation operators are special cases of the existing aggregation operators that already exist in q-ROFH environments.The main function of these operators is to help decision-makers gain a complete understanding of uncertain facts.The proposed aggregation operator is applied to a decision-making problem,with the aim of selecting the most promising real estate project for investment.

关键词： Hypersoft set q-rung orthopair fuzzy hypersoft set induced aggregation operator decision-making

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A Novel Feature Aggregation Approach for Image Retrieval Using Local and Global Features

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 239-262页

作者： Yuhua Li Zhiqiang He Junxia Ma Zhifeng Zhang Wangwei Zhang Prasenjit Chatterjee dragan pamucar Software Engineering College Zhengzhou University of Light IndustryZhengzhou450001China China Mobile Group Henan Company Limited Xinxiang453000China Department of Mechanical Engineering MCKV Institute of EngineeringHowrah711204India Department of Logistics University of Defence in BelgradeBelgrade11000Serbia

The current deep convolution features based on retrievalmethods cannot fully use the characteristics of the salient image regions.Also,they cannot effectively suppress the background noises,so it is a challenging task to retrieve objects in cluttered scenarios.To solve the problem,we propose a new image retrieval method that employs a novel feature aggregation approach with an attention mechanism and utilizes a combination of local and global features.The method first extracts global and local features of the input image and then selects keypoints from local features by using the attention mechanism.After that,the feature aggregation mechanism aggregates the keypoints to a compact vector representation according to the scores evaluated by the attention mechanism.The core of the aggregation mechanism is to allow features with high scores to participate in residual operations of all cluster centers.Finally,we get the improved image representation by fusing aggregated feature descriptor and global feature of the input image.To effectively evaluate the proposedmethod,we have carried out a series of experiments on large-scale image datasets and compared them with other state-of-the-art methods.Experiments show that this method greatly improves the precision of image retrieval and computational efficiency.

关键词： Attention mechanism image retrieval descriptor aggregation convolutional neural network

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Spherical Linear Diophantine Fuzzy Sets with Modeling Uncertainties in MCDM

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Computer Modeling in Engineering & Sciences 2021年第3期126卷 1125-1164页

作者： Muhammad Riaz Masooma Raza Hashmi dragan pamucar Yu-Ming Chu Department of Mathematics University of the PunjabLahore54590Pakistan Department of Logistics Military academyUniversity of DefenceBelgrade11000Serbia Department of Mathematics Huzhou UniversityHuzhou313000China Hunan Provincial Key Laboratory of MathematicalModeling and Analysis in Engineering Changsha University of Science&TechnologyChangsha410114China

The existing concepts of picture fuzzy sets(PFS),spherical fuzzy sets(SFSs),T-spherical fuzzy sets(T-SFSs)and neutrosophic sets(NSs)have numerous applications in decision-making problems,but they have various strict limitations for their satisfaction,dissatisfaction,abstain or refusal grades.To relax these strict constraints,we introduce the concept of spherical linearDiophantine fuzzy sets(SLDFSs)with the inclusion of reference or control parameters.A SLDFSwith parameterizations process is very helpful formodeling uncertainties in themulti-criteria decisionmaking(MCDM)process.SLDFSs can classify a physical systemwith the help of reference parameters.We discuss various real-life applications of SLDFSs towards digital image processing,network systems,vote casting,electrical engineering,medication,and selection of optimal choice.We show some drawbacks of operations of picture fuzzy sets and their corresponding aggregation operators.Some new operations on picture fuzzy sets are also introduced.Some fundamental operations on SLDFSs and different types of score functions of spherical linear Diophantine fuzzy numbers(SLDFNs)are proposed.New aggregation operators named spherical linear Diophantine fuzzy weighted geometric aggregation(SLDFWGA)and spherical linear Diophantine fuzzy weighted average aggregation(SLDFWAA)operators are developed for a robust MCDM approach.An application of the proposed methodology with SLDF information is illustrated.The comparison analysis of the final ranking is also given to demonstrate the validity,feasibility,and efficiency of the proposed MCDM approach.

关键词： Spherical linear Diophantine fuzzy set new operations of picture fuzzy sets spherical linear Diophantine fuzzy weighted geometric aggregation operator spherical linear Diophantine fuzzy weighted average aggregation operator MCDM

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