Fuzzy Decision Model Research Articles

The Validity of Visual Communication Design of Online Advertisement Based on Interactivity

Today's society is a society of Internet information, especially the rapid development of new media that is the most distinctive project today. New media has changed the way traditional media are disseminated, and the advertising industry has made new developments with the rapid development of new media, especially with the rise of online advertising, making the dissemination of advertising more influential. In modern advertising information, online advertising has become very influential. This study aims at how to achieve user-matching push and set the right price is the two major problems of Internet advertising push, through the analysis of the emergence and development of network advertising and visual elements in network advertising, starting from the problems appearing in modern online advertisements, and studying the visual communication design in online advertisements. In this paper, a neural network model and a fuzzy comprehensive evaluation decision model are designed; so as to make our online advertisements rich in national characteristics and really cause online users' active clicks. The experimental results show that the proposed method achieves good results and verifies the effectiveness of the model.

Understanding the financial innovation priorities for renewable energy investors via QFD-based picture fuzzy and rough numbers

This study evaluates financial innovation priorities for renewable energy investors by generating a novel hybrid fuzzy decision-making model. First, SERVQUAL-based customer needs for financial innovation are weighted with decision-making trial and evaluation laboratory based on picture fuzzy sets. Second, the financial innovation priorities are ranked by technique for order preference by similarity to ideal solutions based on picture fuzzy rough sets. In this process, Theory of the solution of inventive problems-based technical characteristics for financial services, the process for innovative services, and competencies for financial innovation are considered using quality function deployment phases. In addition, the Vise Kriterijumska Optimizacija I Kompromisno Resenje method is also considered for an alternative ranking. Similarly, sensitivity analysis is also performed by considering five different cases. It is determined that the ranking priorities based on the proposed model are almost identical, demonstrating the proposed model’s validity and reliability. Assurance is the most crucial factor for the customer needs regarding the financial innovation priorities for renewable energy investors. Concerning the financial innovation priorities, the product is the essential priority for financial innovation; hence, it is recommended that companies engage qualified employees to effectively design the financial innovation for renewable energy investors. Additionally, necessary training should be given to the employees who currently work in the company, which can increase the renewable energy investors’ trust in the innovative financial products. Companies should mainly focus on the product to provide better financial innovation to attract renewable energy investors. An effectively designed financial innovation product can help solve the financing problem of renewable energy investors.

A q-rung orthopair fuzzy decision-making model with new score function and best-worst method for manufacturer selection

Selecting the appropriate manufacturer is important in customized product development because a poor selection may delay the delivery schedule, increase costs, and even affect product quality. This selection task inherently involves ambiguous and imprecise information from decision-makers. The q-rung orthopair fuzzy (q-ROF) sets theory has been proven as a valuable instrument to model human uncertain expressions. However, the existing q-ROF score functions, an essential tool to rank q-ROF values, have some deficiencies, such as generating counterintuitive solutions and antilogarithm or division by zero problems. Furthermore, little research presented the modified best-worst methods (BWM) under q-ROF environments. Nevertheless, these models only generate crisp weights rather than q-ROF weights, which is against the original intention of the q-ROF BWM. To address these problems, this paper first introduces a novel q-ROF score function for measuring q-ROF values. A new q-ROF-BWM is then presented based on the q-ROF preference relations to determine the fuzzy criteria weights. Subsequently, an improved weighted aggregated sum product assessment (WASPAS) with q-ROF settings is presented. Based on them, an integrated q-ROF-BWM-WASPAS model is introduced to rank manufacturers. Additionally, a case study, sensitivity analysis, and several comparisons are conducted to illustrate and validate the usefulness of the developed model.

AG600 Amphibious Aircraft Selection Model Based on Improved Fuzzy Evaluation Algorithm

Aiming at the selection research gap of AG600 amphibious aircraft for salvage of life at sea, the selection index system of AG600 has been established, and an interval intuitionistic fuzzy selection decision model based on fuzzy entropy and score function is constructed. An evaluation index system with 8 indexes was determined based on the rules of the applicability, safety, and accessibility by questionnaire and statistical analysis. Different from the traditional method of determining the fuzzy number of each index solely by experts' scoring, this paper establishes a 5-grade fuzzy evaluation grade and index membership function, and the interval intuitionistic fuzzy number of the index is determined by combining subjective and objective methods. Fuzzy entropy and score function are used to calculate the weights of each index and AG600 selection score for different accidents, so as to obtain the matching degree of AG600 selection in marine life accidents more scientifically and reasonably. Finally, the effectiveness of the proposed model is demonstrated by a practical case.

Analysis of crowdfunding platforms for microgrid project investors via a q-rung orthopair fuzzy hybrid decision-making approach

Effective crowdfunding platforms positively contribute toward improving microgrid energy management systems. Therefore, a comprehensive analysis is required to understand the key factors responsible for success in crowdfunding systems by considering various criteria. This study evaluates crowdfunding platforms for microgrid project investments. In this context, a novel fuzzy decision-making model that includes two different stages is proposed. First, the selected criteria for the crowdfunding platforms for microgrid project investments are evaluated. Second, alternatives, regarding the microgrid project investments, are ranked. In this process, a multi-stepwise weight assessment ratio analysis (M-SWARA) approach based on q-rung orthopair fuzzy sets (q-ROFSs) is considered. Intuitionistic and Pythagorean fuzzy sets are also used in the calculation process to make a comparative evaluation. Similarly, a sensitivity analysis of the ranking alternatives is also conducted with 12 different q values. All the results are rather similar; thus, the findings are reliable. Another model is also created for this purpose with the help of the decision-making trial and evaluation laboratory (DEMATEL) and the technique for order preference by similarity to ideal solution methodologies to check the performance of the proposed model. It is defined that by considering the q-ROF DEMATEL weights, the ranking results vary for different cases. The proposed model with a M-SWARA is more reliable than the model created via the DEMATEL method. This situation provides information regarding the superiority of the model proposed in this study. It is concluded that security is the most important factor in crowdfunding platforms for smart-grid project investors. Additionally, solar panels and energy storage systems/batteries are the most significant alternatives for microgrid project investors. Necessary measures should be taken to forestall the risk of fraud that may occur on this platform. Therefore, the website to be established must be secure against possible hacking attacks. Another important conclusion of this study is that solar panels should be preliminarily developed to increase the effectiveness of microgrid systems.

Fuzzy Multi Criteria Decision Making Model for Agritourism Location Selection: A Case Study in Vietnam

Agritourism is an increasing new trend in the global tourism industry. Vietnam has a long tradition of agricultural production combined with diverse natural resources stretching from the north to the south, bringing advantages in the development of agritourism. The study aims to choose the most appropriate agritourism location in Vietnam for long-term investment. A hybrid fuzzy multi-criteria decision model (FMCDM) is proposed to find the optimal location based on eco-nomic, social, and environmental factors. In the first stage, the fuzzy analytic hierarchy process (FAHP) is used to estimate the relative criteria rating through the evaluation process. In the second stage, the fuzzy technique for order preference using similarities to ideal solution (FTOPSIS) is applied to rank the potential alternative locations. Finally, the best alternative to tourist site investment is Can Tho (A8), which maximizes resources and enhances the local benefits. Future research can also be used to support similar site-selection processes in other regions or could be applied to other types of tourism.

A GIS-based framework using fuzzy relation equation system solutions in urban planning

Several fuzzy decision models are proposed in literature to solve urban planning problems. In this research we present a novel GIS-based framework to solve decision problems in urban planning based on a System of Fuzzy Relation Equations in which the unknowns represent characteristics affecting observable facts constituting the input variables. Aim of this research is to partition the urban study area into subzones, each of which identifies a sub-area of the study area within which the set of analyzed characteristics are homogeneous. The study area is initially decomposed in atomic urban areas called microzones; for each microzone are calculated the greatest and lowest solutions of a System of Fuzzy Relation Equations by using the Universal solution Algorithm and are calculated and fuzzified the values of the output variables. Spatially adjoining microzones with same output variables are dissolved forming homogeneous urban areas with reference to the problem analyzed, called Urban Contexts. For each output variable a thematic map is constructed; in addition, a thematic map of its reliability is created. This framework is tested on a study area given by the district of Ponticelli in the municipality of Naples (Italy); comparison tests performed with respect to a previous GIS-based framework based on a System of Fuzzy Relation Equations show that our method provides a more detailed knowledge of the characteristics of the urban study area with reference to the problem dealt with.

An Integrated Decision-Making Approach Based on q-Rung Orthopair Fuzzy Sets in Service Industry

This study defines key issues for sustainable healthcare policy in COVID-19 period. For this purpose, 9 different criteria that affect vaccine hesitancy are selected with the help of a detailed literature evaluation. A novel hybrid fuzzy decision-making model is developed using DEMATEL and TOPSIS based on q-Rung orthopair fuzzy sets. A comparative evaluation has also been performed using IF DEMATEL and PF DEMATEL. The results of all different methods are almost the same that indicates the reliability and coherency of the proposed model. The findings demonstrate that religion is the most critical factor that causes vaccine hesitancy. It is also defined that active population in daily life is the most important alternative. Developing countries should mainly focus on the actions regarding the religious issues to have sustainable healthcare policies in COVID-19 period. In this context, religious leaders can be released to the media and give information that the vaccine is not against religious rules. This has a significant contribution to convince people who are against the vaccine. Furthermore, these countries should also give priorities to the active population in daily life. Because this group supports the workforce in the country very seriously, it can be possible to increase the workforce in the country by completing the vaccination of this group that helps to boost economic development.

Fuzzy decision-making model for process quality improvement of machine tool industry chain

Industry 4.0 has fostered innovation in industries around the world. Manufacturing industries in particular are advancing towards smart manufacturing by integrating and applying relevant technologies. The output value of machine tools in Taiwan is among the top of the world and the central region is a key area for this industry chain, which supplies manufacturers in Taiwan and their international downstream customers. To support innovation in this industry, the current study used the Six Sigma quality indices for smaller-the-better, larger-the-better, and nominal-the-best quality characteristics to construct a fuzzy decision-making model. Based on this model, we propose a process quality fuzzy analysis chart (PQFAC) for process quality improvement. Our use of fuzzy decision values to replace lower confidence limits decreases the probability of misjudgment made by sampling errors. The proposed fuzzy model also offers a more accurate assessment of process improvement requirements. We provide a real-world example to demonstrate the applicability of the proposed approach. Machine tool manufacturers can apply the platform and proposed model to evaluate their process capabilities for the vital parts suppliers and downstream customers, determine optimal machine parameter settings for processes with inadequate accuracy or precision, establish more suitable machine repair and maintenance systems, and combine the improvement experiences of customers to create an improvement knowledge base. This will enhance product value and industry competitiveness for the entire machine tool industry chain.

Risk evaluation of excavation based on fuzzy decision-making model

A risk evaluation model for an excavation system incorporating the technique for order preference by similarity to an ideal solution with hybrid fuzzy sets (Pythagorean and triangular fuzzy sets) is developed. The proposed model consists of three phases: i) decision hierarchy construction, ii) multi-source data integration, and iii) high-risk factor identification. The proposed model was implemented considering expert judgements and monitoring data. To increase the reliability of the results, expert confidence index was utilized to assign relevant importance of judgements from consulted experts. The proposed model was adopted to identify high-risk factors. An excavation engineering project in Tianjin was applied to evaluate the performance of developed model. Results show that high-risk factors were identified. The developed model can be employed as a decision-making tool towards project managers for construction risk analysis and evaluation.

An extended hybrid fuzzy multi-criteria decision model for sustainable and resilient supplier selection.

The formalization and solution of supplier selection problems (SSPs) based on sustainable (economic, environmental, and social) indicators have become a fundamental tool to perform a strategic analysis of the whole supply chain process and maximize the competitive advantage of firms. Over the last decade, sustainability issues have been often considered in combination with resilient indexes leading to the study of sustainable-resilient supplier selection problems (SRSSPs). The current research on sustainable development, particularly concerned with the strong impact that the recent COVID-19 pandemic has had on supply chains, has been paying increasing attention to the resilience concept and its role within SSPs. This study proposes a hybrid fuzzy multi-criteria decision making (MCDM) method to solve SRSSPs. The fuzzy best-worst method is used first to determine the importance weights of the selection criteria. A combined grey relational analysis and the technique for order of preference by similarity to ideal solution (TOPSIS) method is used next to evaluate the suppliers in a fuzzy environment. Triangular fuzzy numbers (TFNs) are used to express the weights of criteria and alternatives to account for the ambiguity and uncertainty inherent to subjective evaluations. However, the proposed method can be easily extended to other fuzzy settings depending on the uncertainty facing managers and decision-makers. A real-life application is presented to demonstrate the applicability and efficacy of the proposed model. Sixteen evaluation criteria are identified and classified as economic, environmental, social, or resilient. The results obtained through the case study show that “pollution control,” “environmental management system,” and “risk awareness” are the most influential criteria when studying SRSSPs related to the manufacturing industry. Finally, three different sensitivity analysis methods are applied to validate the robustness of the proposed framework, namely, changing the weights of the criteria, comparing the results with those of other common fuzzy MCDM methods, and changing the components of the principal decision matrix.

Fuzzy Based Reliable Load Balanced Routing Approach for Ad hoc Sensor Networks

Energy management and packet delivery rate are the important factors in ad hoc networks. It is the major network where nodes share the information without administration. Due to the mobility of nodes, maximum energy is spent on transmission of packets. Mostly energy is wasted on packet dropping and false route discovery. In this research work, Fuzzy Based Reliable Load Balanced Routing Approach (RLRA) is proposed to provide high energy efficiency and more network lifetime using optimal multicast route discovery mechanism. It contains three phases. In first phase, optimal multicast route discovery is initiated to resolve the link failures. In second phase, the link quality is estimated and set to threshold value to meet the requirements of high energy efficiency. In third phase, energy model is shown to obtain total energy of network after transmission of packets. A multicast routing is established Based on path reliability and fault tolerant calculation is done and integrated with multicast routing. The routes can withstand the malicious issues. Fuzzy decision model is integrated with propose protocol to decide the performance of network lifetime. The network simulation tool is used for evaluating the RLRA with existing schemes and performance of RLRA is good compared to others.

Fuzzy Decision Model: Evaluating and Selecting Open Banking Business Partners

The finance supply chain has always been a different supply chain compared to product supply chain being a service supply chain. Open Banking (OB) is one of the most important milestones since the beginning of financial technology innovation and service supply chain. As these are activities provided by traditional banks, non-bank financial institutions also provide financial service with access to consumer banking, transactional and other financial data to develop financial applications and services tailored to their customers. The development of financial technology, “Open banking”, promotes financial services to begin this transformation. However, evaluating and selecting open banking business partners from multiple perspectives for banks are underexplored. Thus, the authors in this research proposed a hybrid multi-criteria decision-making model which includes a Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) model and a Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) model with supports from seasoned domain experts. The contribution of this research is a proposition of a fuzzy decision model for evaluating and selecting open banking business partners. The model successfully determined a suitable open bank provider in order to assist decision makers decide objectively.

Evaluating Smart Agricultural Production Efficiency using Fuzzy MARCOS method

Agricultural production efficiency can be improved, the environment can be improved, and sustainable agricultural development can be achieved with smart agriculture. Several nations and businesses are devoted to developing or introducing innovative agricultural practices and technologies. As long as conventional agricultural management systems are in place, it's going to be tough for businesses to choose and apply smart agriculture solutions without running into stiff competition. As a result, businesses must weigh their options and choose a workable solution ahead of time. There is a novel fuzzy multi-criteria decision model presented in this research that may be used to evaluate the agriculture solution. We created fuzzy MARCOS, which uses the COmpromise Solution's Measurement Alternatives and Ranking Method (fuzzy MARCOS). Triangular fuzzy numbers (TFNs) linguistic dimension was also developed. According to this technique, the criteria weights used to evaluate the road network parts were calculated. Consequently, it is clear that a certain part of agriculture is affected by the study results.

Sustainable supplier evaluation and selection in developing countries: an integrated fuzzy framework

Evaluation and selection of suppliers play a vital role in improving their sustainability performance. In this regard, evaluating the sustainability performance of suppliers is much more critical in developing countries. These countries are among the best destinations for supplying raw materials and workforce utilisation because of lower costs. In recent years, various multi-criteria decision aiding (MCDA) frameworks utilised to evaluate suppliers based on sustainable aspects. However, few studies analysed the evaluation and selection of suppliers' sustainability in an integrated fuzzy MCDA model regarding the supplier selection process and developing nations. This research presents a comprehensive, integrated fuzzy decision model for sustainable supplier evaluation and selection using an integrated FDelphi-FDEMATEL-FANP-FVIKOR by considering sustainability (social, economic and environmental). Also, this research provides a comprehensive analysis of MCDA techniques' literature in terms of the main steps of sustainable supplier selection (e.g., formulation of criteria, handling of criterion's interaction, weight calculation and final selection). The previous research rarely considered the evolution of suppliers' sustainability according to its steps in the literature. This framework assists supply chain managers and practitioners better understand the extraordinary role of these steps on businesses. This research's proposed framework is validated by a numerical example taken from Iran's automotive industry.

Construction and Application of Economic Management Fuzzy Decision Model Based on Fuzzy Relevance Method

Fuzzy mathematics has been used more and more extensively and deeply in various fields since its emergence, and it has been more and more recognized. However, there is less research on fuzzy mathematics in the field of economic management. This paper attempts to introduce fuzzy theories and methods into economic management. On the premise of introducing fuzzy decision‐making theories and methods, several fuzzy theories and methods that are commonly used in economic management are given. The fuzzy correlation method of economic phenomena is proposed, and the method is used to study the correlation between economic phenomena. Besides, this paper also uses fuzzy methods to analyze the relationship between economic phenomena—fuzzy relevance, and uses fuzzy two‐way decision‐making methods to carry out economic management and decision‐making so that the decision‐making results are more reasonable, scientific, and operability. In addition, this paper rationally explains the phenomena and laws existing in economic activities from a fuzzy perspective and gives models for fuzzy inference, diagnosis, and production decision‐making, which have important meaning and profound theoretical value and practical value.

An integrated framework for new sustainable waste-to-energy technology selection and risk assessment: An R-TODIM-R-MULTIMOOSRAL approach

Safe and economical solid and hazardous waste disposal is a serious global concern and challenge. Therefore, the production of new environmentally friendly products for safe waste disposal and its conversion into energy is a major need of governments and companies manufacturing waste management products. An R&D project mainly aims to present an innovation and solve a problem in the real world, and is thus very expensive and riddled with technical complexity and uncertainty. The accurate selection of the project and its risk assessment improve the project's success rate and limit the companies' costs. This article aims to review the important criteria for the accurate selection of new projects for safe municipal solid waste disposal and also to identify and evaluate the risk factors affecting these projects' performance. Herein, a new fuzzy risk-based decision model is presented to achieve the stated goals. This model utilizes MCDM methods and develops them based on R-numbers method which has recently been developed to model the risk of fuzzy numbers in dynamic environments. The proposed model is successfully applied in the Waste Management Organization of Tehran, Iran. The results reveal the superiority of the plasma pyrolysis system project over other projects, e.g., launching power plants based on landfill, plasma gasification, and waste pyrolysis system. The scores of the four technologies evaluated based on the R-TODIM method are1, 0.835, 0.789, and 0, respectively. The results of risk assessment of the plasma pyrolysis system also indicate the importance of risk factors, e.g., access to technology and laboratory equipment and new scientific resources, compared to other risk factors. The scores of the three mentioned risk factors are 4.864, 4.798, and 1.1, respectively.

A Many-Objective Multistage Optimization-Based Fuzzy Decision-Making Model for Coal Production Prediction

The traditional coal energy system cannot meet the requirements of modern industrial production for sustainable development because extensive economic growth has caused significant coal consumption. Therefore, it is particularly important to formulate an effective coal production decision-making scheme to support complex coal production systems for sustainable development. To address this challenge, an improved many-objective fuzzy decision-making model with human participation for coal production prediction is established in this article. The model includes five objective functions: economic, energetic, environmental, coal gangue, and safety profits. Moreover, a novel multistage many-objective optimization algorithm is designed to adjust relevant model parameters and make the obtained model solution more accurate when compared with involved algorithms. In the algorithm, there are three stages of optimization. In stage one, the model with the relatively good convergence solution is chosen. In stage two, the diversity of the model solution needs to be retained by employing a diversity maintenance mechanism. In stage three, a comprehensive measurement approach is designed to ensure the balance of convergence and diversity during population evolution. To verify the validity of the model and the superiority of the optimization algorithm, five other advanced many-objective algorithms are used for comparison. The results from simulations on a benchmark function show that the proposed optimization algorithm has better performance than involved algorithms. Model solutions obtained on different metrics have better convergence and diversity for the coal fuzzy decision-making problem.

Retracted: Fuzzy based rendezvous points selection for mobile data gathering in wireless sensor network

AbstractIn this article, fuzzy based rendezvous points selection (FRPS) technique for mobile data gathering in wireless sensor networks is proposed. At beginning, the importance of detected data is categorized into perilous and non‐perilous based on the perseverance and time limit of data. Then two Sencars are set up: one for gathering perilous data by visiting straight to the corresponding sensors, the other one for gathering non‐perilous data from other sensors. The rendezvous points (RPs) for visiting the Sencars are determined by using fuzzy decision model based on the input parameters energy level, buffer occupancy level, and current load. Based on the output of this model, each sensor is assigned a combined utility value. Then the sensors with high utility values are selected as RPs and the details are broadcast to the respective Sencars.

A novel intuitionistic fuzzy three-way decision model based on an intuitionistic fuzzy incomplete information system

A novel intuitionistic fuzzy three-way decision model based on an intuitionistic fuzzy incomplete information system