fuzzy inference system

Risk management in the supply chain and its impact on the competitiveness, dynamism and agility of large industries is considered a key factor in capturing market share, reducing the influence of competitors, and the survival of large companies. In this new approach, by introducing a systematic model to extract each risk from the subset to which it belongs, we identify the risks in the system and calculate the amount of each risk using the risk priority score method. Then, by introducing a three-level fuzzy inference system model as a powerful tool to analyze and assess the impact of each risk in supply chain management, we will analyze the results. The final model was implemented in the Guilan Tobacco Company as a case study, the fuzzy inference system model was implemented with MATLAB software, and the results obtained were compared with the weighted simple sum fuzzy decision-making method, and the accuracy of the results was confirmed. This research, by introducing a comprehensive model for controlling and extracting all potential risks in the supply chain and analyzing each risk through a fuzzy inference system, can help managers improve productivity and competitiveness due to its high performance and speed of response in uncertain conditions. One of the most important results of this research is the identification of the status of each risk, which senior managers must quickly control by taking corrective actions for these risks (risks that are at a medium or high level).

When two or more organizations seek to enhance their collaboration, they may opt to form strategic alliances. These alliances aim to progress cooperatively toward shared goals through resource sharing, while maintaining their independence. Maturity models are essential tools for aiding manufacturing organizations in developing their partnerships. However, there is a lack of empirical research on creating a strategic alliance maturity model with clear guidelines. Consequently, no existing model effectively measures the maturity of a strategic alliance, particularly one that can address inaccuracies due to human judgment and inherent evaluation uncertainties. This research aims to design a method to assess the maturity level of strategic alliances, providing a better understanding of the current state of cooperation based on strategic alliance maturity criteria.

This research developed a maturity model using fuzzy logic to evaluate the status of a collaboration at a specific point in time. The strategic alliance maturity model, based on fuzzy logic, was created through a clear and precise procedure and a multi-method approach, including literature reviews, interviews, focus groups, and case studies. A weighted fuzzy inference system combined with the fuzzy data envelopment analysis technique was employed to measure maturity levels using specific indicators. Additionally, a genetic algorithm was applied to generate a set of fuzzy rules. Like all maturity models, the one developed in this research consists of two main components: the maturity levels of the strategic alliance and its maturity dimensions.

The research model defines five levels of maturity: ad-hoc, initial, managed, planned, and optimized strategic alliance. A 44-item list of indicators for measuring strategic alliance maturity was compiled from articles, expert interviews, and analysis of successful and unsuccessful alliances. This list was then categorized into 17 criteria across six dimensions. Essential indicators were identified using the content validity ratio technique, and their relative importance was determined through data envelopment analysis. Expert surveys were used to create fuzzy sets for the variables of the fuzzy inference system. Additionally, a set of fuzzy rules was developed by examining examples of strategic alliances both domestically and internationally, and refining them through expert surveys.

The proposed model has been evaluated and validated through a real case study involving collaboration between a manufacturing organization and its business partner. The research results demonstrate that this approach offers a robust and practical diagnostic tool based on a set of strategic alliance maturity indicators. By analyzing the gaps identified by this model, an action plan can be devised to enhance the maturity level of the strategic alliance.

Identifying the best candidates for commanding and management jobs in military organizations is of double importance, considering their vital role in the management of the battlefield. Therefore, the present research aims to design an intelligent system for identifying the optimal candidates for the mentioned jobs with the help of fuzzy inference system. This research is of a developmental-applied type, which was done in an explanatory-case method. For this purpose, two main steps were taken, which include the systematic review of scientific texts in order to identify the optimal talent indicators and the design of a two-level intelligent system to identify the optimal candidates for command and management jobs by combining the theory of fuzzy sets and fuzzy logic. A sample of 15 commanders, managers and experts in the field of human resource management and talent management in the Islamic Republic of Iran Army was selected as a sample of experts to design and create fuzzy rules in a purposeful judgmental way. The validation of the system was also confirmed by examining its behavioral output and comparing it with the actual results of employee evaluation management.The findings of the systematic review of scientific literature showed that the optimal indicators can be classified into three categories, including commitment combined with enthusiasm, competencies and alignment with the organization's goals and strategies, under which 12 indicators were placed. These 12 indicators were entered into the second-level fuzzy inference system as input indicators through the user interface, whose output models the level of commitment indicators combined with enthusiasm, competences and alignment with the goals and strategies of the organization. Also, at the next level, three indicators were entered into the system as input indicators, the output of which is the degree of optimization of talent for command and management jobs in five levels: high-risk talent, weak talent, equipping talent, misguided enthusiasm, and optimal talent. Modeled. The system designed with 90% accuracy has simulated the behavioral output of evaluation management to identify the optimal candidates at five levels.

This research aims to develop a robust predictive model for evaluating the strategic cooperation between Iran's banking system and fintech startups. Leveraging insights from 14 experts within the banking and fintech sectors, a hybrid methodology involving the foundation's interview tool and data analysis was employed. Thirty-one indicators, categorized into six key factors influencing strategic cooperation, were identified. Using a fuzzy approach and MATLAB software, a conceptual model was crafted to assess the strategic cooperation of the banking system with fintech startups. Input from 320 industry professionals and managers further enriched the analysis. The findings underscore the pivotal dimensions shaping this cooperation, including barriers to entry, external factors, explanatory elements, varying cooperation levels, consequences of collaboration, and the motivations driving banks and fintechs. The level of strategic cooperation was determined to be in the medium to high range. Notably, the Anfis-designed model exhibited acceptable validity and predictive power. This study contributes not only by unraveling critical cooperation dimensions but also by furnishing a reliable predictive tool. The comprehensive approach, amalgamating expert insights, diverse indicators, and advanced analytical tools offers valuable insights to fortify strategic cooperation in the banking-fintech nexus.

the present study aims to design an optimal model for organizational entrepreneurship based on corporate governance. This research employs a mixed-method and multi-method approach (qualitative-quantitative-quantitative). In the qualitative section, content analysis is used as the research method. The statistical population in the qualitative part consists of experts and managers of the Industry and Mine Bank, selected through purposive and snowball sampling methods. Data collection tools involve semi-structured interviews, which continued until theoretical saturation was achieved, with the twelfth interview confirming this. Data analysis was performed using MAXQDA 2020 software through three stages of coding. In the second part and the quantitative section of the research, the structural equation modeling method is employed with the SPSS and SMART PLS software. The statistical population in the quantitative section includes employees, deputies, and managers of the Industry and Mine Bank, with a sample size of 226 individuals determined using Cochran's formula. In the quantitative part, the statistical analysis of data was conducted using fuzzy control in MATLAB software, resulting in the optimal model. The research findings indicate that achieving a desirable state for having an organization that is entrepreneurial based on corporate governance in Iranian government organizations is the result of a combination of performance, responsiveness, transparency, board structure, stakeholder rights, and professional ethics dimensions. In this process, the role of skill development and organizational learning is crucial.

Innovative human resources are the driving engine behind dynamic thinking, critical approaches, and are essential catalysts for transformation and innovation within any organization. They embody the questioning spirit and proactive nature of a vibrant workforce, which is critical to the organization's continual evolution. Effective initiation, sustained development, and strengthening of such innovative practices are not feasible without the solid foundation of both material and spiritual support as perceived by the employees. Consequently, the central aim of this research is to meticulously design and present an innovative human resources model based significantly on perceived organizational support, meticulously studied within the government organizations of Qazvin.

This research employs a comprehensive mixed exploratory approach, amalgamating qualitative and quantitative methodologies, which is applied in intent and descriptive-survey in its implementation method. The qualitative component of this study utilized the acclaimed Clark and Brown thematic analysis method with a comparative approach. In this phase, the statistical sample, carefully chosen using a non-probability targeted method, included a select group of 11 academic and organizational experts. The quantitative segment focused on a diverse group of managers, deputies, and specialists from various government organizations across Qazvin, encompassing sectors such as governance, infrastructure, economic, social, cultural, and service affairs. The robust sample size, meticulously calculated using Cochran’s formula, was set at 220 individuals. The sampling strategy in the quantitative phase began with targeted selection, followed by a strategic combination of cluster, stratified, and simple random sampling methods. Data collection was comprehensively conducted using semi-structured interviews and a meticulously developed questionnaire. For data analysis, the qualitative section leveraged MaxQDA software for detailed triple coding—axial, selective, and open coding. Simultaneously, the quantitative section employed SPSS for rigorous data analysis and MATLAB software for the sophisticated implementation of confirmatory factor analysis to validate the model and determine the optimal combination of perceived organizational support and innovative human resources.

The research findings robustly highlight the initial identification of themes at comprehensive, organizing, and basic levels. This thorough investigation led to the emergence of innovative themes that were distinctly extracted from the disparities between the theoretical literature, past research background, and enriched insights obtained from expert interviews. The elaborately finalized model successfully integrates essential local government indicators along with moral and human values, thoughtfully adapted to the unique cultural context governing the administrative operations in Qazvin. This adaptation drew extensively from an in-depth review of pertinent laws, regulations, and foundational documents.

One of the paramount achievements of this research is the formulation of a multi-level model that proficiently measures the optimal combination of organizational support factors and innovative human resources within Qazvin's government organizations. This model is innovatively designed to predict the output variable based on the carefully evaluated values of the input variables. The strategic implementation and meticulous execution of this model within the government organizations of Qazvin can be considered exceedingly practical and unequivocally beneficial, aiming to significantly enhance the operational effectiveness and efficiency within these institutions.

Railway cargo transportation has gained popularity as a preferred mode of conveyance, owing to its benefits over alternative methods. These advantages include reduced pollution in comparison to road transport, shorter transit times compared to maritime shipping, and cost-effectiveness in contrast to air transport. As a result, transport companies are more willing to use rail freight transport, especially for international transportation. One of the challenges facing the Islamic Republic of Iran Railways is to rank these companies, which are influential in various decisions such as granting discounts and facilities, handing over foreign wagons, prioritizing requests, and handling complaints. Therefore, this paper aims to develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies.

Considering the dynamic nature of the problem, the dynamic multi-criteria decision-making model was implemented based on the real data of international freight transportation (including export, import, and transit) by international rail transport companies in five years. To estimate future data for international rail transport companies, the fuzzy inference system was used. A fuzzy inference system is a mathematical tool that can handle uncertainty and imprecision in data.

Considering the dual uncertainty factors initially, companies' activities were evaluated using a fuzzy inference system, followed by the dynamics of a dynamic multi-criteria decision-making model the prioritization of 15 international rail transport companies for the Islamic Republic of Iran Railways was established. The results indicated that dealing with the problem of ranking international rail transport companies dynamically will achieve a more favorable outcome. The applicability of the presented method to benefit from the fuzzy inference system in the dynamic multi-criteria decision-making model is one of the other results.

This study could develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies. The proposed dynamic multi-criteria decision-making model includes several implications. Firstly, it provides a comprehensive and systematic approach to evaluate and rank international rail transport companies based on multiple criteria and factors. This approach can help decision-makers to make informed decisions and allocate resources effectively. Secondly, the use of the fuzzy inference system to estimate future data is a significant contribution to the field of transportation. This mathematical tool can handle uncertainty and imprecision in data, which is common in transportation systems. This can help decision-makers to anticipate future trends and plan accordingly. Moreover, the proposed model can be customized and applied to other realms and fields. The model can be adapted to the specific needs and requirements of each problem, making it a versatile and adaptable tool for decision-making. By using this model, decision-makers can take into account multiple criteria and factors, such as cost, time, and environmental impact, and evaluate different scenarios to find the most optimal solution.

In today's world where organizations are rapidly changing the diverse demands of customers, the main focus of organizations is on technology and technology development to stay competitive and improve competitive advantage by increasing customer value. The process of choosing between different methods of technology transfer depends on many factors, including technological, organizational, market, human resources, and the environment. The purpose of this paper is to identify the factors affecting the choice of technology transfer method in the transmission line fittings industry and to provide a suitable model for selecting the optimal technology transfer method according to the prevailing conditions in the industry and the characteristics of companies in this industry.

The present study is applied with qualitative, cross-sectional and descriptive variables. The statistical population of the study includes a group of experts, administrators and university experts with a higher degree and at least five years of work experience who were selected non-randomly using targeted and networked judgmental methods. Data collection tools to identify technology transfer methods and influencing factors, library studies and interviews and data collection tools to determine the weight of influencing factors are closed questionnaires whose validity was confirmed by experts. Data were analyzed using descriptive and inferential statistics and SPSS and MATLAB software. Fuzzy inference system has been used to select the optimal method of technology transfer.

By examining the results of questionnaires and consulting experts and adjusting the factors affecting the choice of technology transfer method, 14 factors were identified as system input and seven important and applied technology transfer methods as fuzzy inference system output.

According to the obtained results, the priority of choosing the technology transfer method in the mentioned industry is as follows: 1-joint venture 2-consortium 3- Reverse Engineering 4- Out sourcing 5- Licensing 6- Research& Development 7- R&D Contract

Hundreds of thousands of new businesses are created every year around the world, and it is estimated that about half a billion people worldwide are actively trying to start new businesses. Therefore, creating a new business is very important; because it creates new job opportunities, produces new technology and creates wealth and value in society. However, a large percentage of small businesses fail in the early years of their existence. Meanwhile, in the field of superior technologies, due to the high dependence on changing technology and also the need for high initial capital, firms are facing a more difficult situation. The purpose of this study is to provide a rule based database to determine the success and failure of IT startups in Iran. For this purpose, using research conducted in this field and also receiving the opinions of experts through a semi-open questionnaire, we identified 36 factors affecting the success and failure of startups and classified them into 5 categories. In this research, the method of fuzzy inference system by Mamdani method has been used to analyze the factors. The statistical community is the founders of Iranian startups as well as professors and business professionals;30 founders of successful Iranian startups have been randomly selected as a sample. The results show that team-related factors and team characteristics have the greatest impact on the success and failure of startups.

Leasing companies in the world use various methods to equip and allocate resources, but in Iran these methods are less considered. In order for these methods to be applicable in domestic leasing companies, they must first be analyzed jurisprudentially and if approved, their economic dimensions are also to be examined so that they could be used by leasing companies if, of course, they fit in the economic environment of the country. In this research, after enumerating the methods of equipping and allocating resources in leasing companies in the world, the jurisprudential details of each method are studied and then following the opinions of economic jurisprudence experts and the multi-stage ijtihad method, the application of these methods have been approved. Furthermore, by using the industry experts’ opinions, the efficiency conditions of these tools in the economic environment of the country have been evaluated. Afterwards, the criteria for using the above methods in the structure of equipping and allocating these companies' resources have been extracted by interviewing industry experts, and finally, with the use of ANFIS method a model for Iranian leasing companies has been presented. The research findings indicate the enumeration of methods of equipping and allocating resources of leasing companies, jurisprudential validation of these methods, their financial validation, extraction of the necessary criteria for designing a model and finally presentation of a model to leasing companies.

Successful organizations in today's turbulent and competitive environment are those that institutionalize innovation in all their functional elements. Based on this, in the present research, a suitable model for evaluating the capacity of organizational innovation is presented.

The current research is applied based on its purpose and descriptive-survey research in terms of data collection method. The statistical population of the study included 28 experts of the studied organization. After identifying the innovation indicators based on the library study, the fuzzy Delphi technique was used to determine the final indicators. Also, fuzzy inference system was used to design the research model. So, 3 sub-FIS and 1 main FIS were designed in MATLAB software, which were used as the basis for innovation capacity evaluation.

The developed model includes 3 dimensions of input status (existence of culture of innovation, transformational leadership, innovation as part of strategy, mission, vision and teamwork), process  status (research and development capacity, open innovation, idea management and research collaborations) and output status (efficiency and effectiveness of innovative products and investment on producing innovative product). To implement the model, data from an military organization was used, which determined the value of organizational innovation capacity as 0.68. The little difference of the system output (0.68) with the average of experts judgements (0.65) about performance of the organizational innovation indicates the validity of the proposed model.

The presented model will provide a suitable tool and perspective for managers to analyze the innovation gap and create improvements in the organization.

Teachers evaluation as one of the essential educational needs has an important role in improving educational quality and developing smart schools. The lack of implementation of a systematic and scientific evaluation system not only will discourage committed teachers, but also will have consequences such as reduced commitment and leave the organizational work. Therefore, the present study attempts to present a fuzzy inference system (FIS) model to evaluate the performance of teachers working in smart schools of Yazd province. In this regard, after reviewing the literature and identifying the factors affecting performance evaluation in two sections of computer literacy and classroom performance, the fuzzy Delphi technique was used to get experts agreement (20 experts) on the model’s final criteria. The ambiguity and complexity of performance appraisal criteria, especially qualitative criteria, as well as the use of verbal variables for extracting expert opinions, led to the use of fuzzy inference system for analyzing model components. In the case of the under study schools, the output of the system indicates that the status of smart school teachers in the basic courses and content production criteria is at intermediate level, in the Internet and educational supplementary software is at a relatively high level, and in the educational content development and electronic evaluation is at a low level. Based on these findings, teachers performance in smart schools was evaluated as medium level. Finally, some suggestions were proposed to improve the current status of smart schools.

Evaluation is one of the most important necessities in the process of urban regeneration, which leads to the optimal decision to solve the problem of urban inefficiency. Evaluating decisions in the urban regeneration process, given its complexity, ambiguity and uncertainty, is a critical issue that requires identifying the criteria that affect its realization and using fuzzy decision-making methods. The purpose of this study is to introduce a fuzzy Delphi method based on qualitative reasoning to identify and localize the criteria affecting urban regeneration. The research method is a combination of quantitative and qualitative methods. The present study is applied in terms of developmental goal and descriptive-analytical in terms of researcher action. The content analysis method was used to develop the conceptual model of the research and the fuzzy inference system (FIS) and Matlab and SPSS software were used to analyze the components and criteria of urban regeneration. To obtain the required data for the research, open and closed questionnaires were distributed among experts and the expert group. Then, the obtained data were entered into the fuzzy inference system and their output was considered as research findings. According to the results of the study, 18 criteria were identified to evaluate the regeneration policy. These criteria were reduced to 12 criteria in the final selection stage, which was done by Delphi interview method. The results show that the most important and insignificant components of urban regeneration in Qom are economic, functional, physical, managerial, social and environmental components, respectively. General Transportation and Access to Urban Management Safety and Health, Housing and Infrastructure.

Applying the system dynamics approach in businesses requires specialized knowledge, in particular, of defining mathematical relationships among variables. This stud seeks to make the use of this approach easier by providing a method for using linguistic variables and the fuzzy inference system in the systems dynamics approach. To evaluate the ease of use and efficiency of the presented method, this method would be used to define the relationship among variables in the purchasing department of a distribution company.

To carry out this research, a literature review was first conducted in the field of fuzzy logic and system dynamics. Next, with the cooperation of an expert from the purchasing department of the distribution company under study, some fuzzy linguistic variables as well as their rules were determined. Finally, the SD model was obtained by using the fuzzy inference system.

The proposed approach can reflect the business dynamics of the distribution company in accordance with what is happening in practice. According to the feedback model feedback and based on the modified linguistic variables, appropriate values were obtained for decision making. In order to evaluate the hybrid approach, a fuzzy inference system was used to calculate the purchase rate according to the two factors of inventory and base sales. These two factors were expressed through linguistic variables by the words "low", "medium", and "high", while the purchase price, as the output of the inference system, was expressed through the five words "very low", "low", "medium", "much", and "too much", according to the expert. After implementing the model, the presented approach (by modifying the fuzzy linguistic variables) was found capable of changing the output to achieve the desired results, as the expert confirmed.

The combined approach can be used in simulating similar cases (where human factor perception and decision-making play a significant role) and can easily reduce the complexity of the required formulas in the system dynamics approach. An important function of the hybrid approach used in this study was to model and simulate the real world in accordance with what is happening in practice.

Car safety systems, as one of the most important parts of the automotive industry, to improve quality and prepare for the future, require the identification of modern technologies and appropriate methods of their transmission.

The aim of this study is to identify the components of improving the transmission capabilities of technology in automotive safety systems and to analyze the relationships between them with a forward-looking approach.

This study is applied and descriptive survey study and based on mathematical modeling with fuzzy inference system. Using previous literature and research studies and interviews with 11 experts from automotive safety systems companies, indicators are identified and categorized. And to analyze the relationships between the factors, the fuzzy inference approach with Matlab software are used.

20 indicators are identified and categorized into 4 components. The localization and deployment component of technology with the first rank and 34.38% weight criterion is the most important variable and the identification and selection of technology with 19.66 weight criterion is in the last rank.

senior executives and automakers should pay special attention to the indicators of the capability of localize and deploy technology.

The purpose of this research is to design an integrated model using a balanced scorecard and fuzzy inference system to evaluate the educational performance and ranking of these institutions with a comprehensive view and high accuracy. The research method is a case study. The statistical population includes 66 university experts in Semnan. Based on Cochran's formula and sample volume formula, 57 people were selected as the statistical sample in factor analysis. Two researcher-made questionnaires were used to identify the indicators and determine the scenery structures of the balanced scorecard. Content validity of the questionnaires was calculated based on the CVR validity index in all items more than 0.29 (for 40 experts and more) . The perspective of the balanced scorecard was examined. The AVE index in financial terms is 0.79, customer 0.89, growth and learning 0.83, internal processes 0.80, which indicates the validity of the appropriate structure. The reliability of the questionnaires was confirmed with Cronbach's alpha values ​​of 0.77 and 0.83, respectively. According to the results of "Roshddanesh" institute or score (0.71), "Koomesh" institute with score (0.62) and "Fazilat" institute with score (0.45) had the best educational performance, respectively. Findings of this study can, in addition to improving the accuracy of calculating the score of educational performance, determine the rank of educational performance of higher education institutions by considering the amount of resources used to achieve it.