جستجوی مقالات مرتبط با کلیدواژه « سیستم استنتاج فازی » در نشریات گروه « مدیریت »

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.

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.

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.

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.

This paper aims to design a fuzzy inference system to evaluate the green supply chain of export manufacturing companies. This research has been applied from the point of view of purpose. The statistical population of this study included export manufacturing companies in the northwest of the country. The statistical sample is targeted, and 143 companies are determined. A research questionnaire based on the research literature was used to collect the data. In order to examine the validity of the questionnaire, while using formal validity, the validity of the structure has been used based on confirmatory factor analysis. Cronbach's alpha coefficient was also used to evaluate the reliability of the questionnaire. The research questionnaires were distributed among the statistical sample members of the research after confirming the validity and reliability. In order to evaluate the green supply chain of companies, a fuzzy inference system has been used based on triangular membership functions and Mamdani inference. The results show that the designed system is able to show how green the supply chain of companies is based on numerical values ​​and linguistic terms.

Social responsibility is a mechanism through which organizations are obligated to use their resources to secure the interests of the society and to improve public welfare. The aim of this research is designing a 3D model of social responsibility for public organizations in Iran. In the first step, using Delphi method, collecting experts’ opinions, and taking the national conditions into consideration, a number of components of social responsibility were identified. They consisted of accountability, economics, respect for cultural-social dignity, observing people’s rights, environmentalism, and finally, respect for spirituality, Islamic values, fairness and justice. In the second step, compatibility was established between the degrees of organization’s ethics and its public issues strategies, and the degrees of the impact of each social responsibility items were identified. To analyze the data, fuzzy inference system was employed. Results show that the impact of ethical approach on social responsibility is upward and positive.

Data envelopment analysis is a well-known method based on mathematicalprogramming to measure the efficiency of decision-making units. This approachidentifies some units as efficient units set. According to these units, it constitutes anefficient frontier. In this case, discernment between efficient decision-making units are isimpossible because several decision-makers have the same efficiency score.

This study presents a new method for ranking efficient units in fuzzy dataenvelopment analysis. In this study, using a fuzzy inference system for ranking efficientunits is proposed as a new method. In the proposed method, the efficient and inefficientunits are first separated from each other using data envelopment analysis. Then, theconcepts of fuzzy inference system are used to rank efficient units.

The information of inefficient units is in a way which the fuzzy dataenvelopment analysis fails to assign an equivalent value of one to these unit’s efficiency.According to this concept, in the proposed method, each of these inefficient units isconsidered as a rule, and the amount of these rules are fired by the efficient units, hasused as an indicator for their ranking.

Finally, a numerical example is performed to check the accuracy of themodel's performance. In this example, the data used in one of the basic articles in thisfield were used and it was found that the results obtained from the proposed method arequite similar to the results of the mentioned research.

As policy interventions in financing higher education system in Iran lacks the adequate effectiveness and efficiency, it is necessary to redefine the role of government and other actors in higher education industry. In this study, based on the individual utility and social utility of disciplines, an expert model was developed to determine the appropriate financing mechanism for humanities in bachelor degree. Using the experts’ knowledge along with the literature and fuzzy inference system, an expert model was designed by MATLAB software 2020a. Priority of the field in the “comprehensive scientific map of Iran” and indicators of social and individual utility of disciplines (extracted by research team) were used as inputs of model. A software was developed which presents the final model to determine mechanism of financing disciplines. Study of two sample disciplines resulted in “Market” group mechanisms for Law and “Government” mechanisms for Persian Language and Literature. The results show that simultaneous attention to individual and social utility of disciplines can improve prioritization and policy-making in financing. It can also increase the productivity of investment in higher education. Therefore, using this approach, it is possible to redefine the role of government and regulate the relations between actors in the higher education industry in order to increase the efficiency and effectiveness of government investment in higher education, implement Development Programs, keep a balance in higher education market, extend market-oriented disciplines, avoid mass production of graduates, and prevent the excessive development of universities and higher education institutes.

The purpose of this study is to provide a model in determining the impact of block-chain technology on supply chain performance in the automobile industry using a fuzzy inference system design. In terms of purpose, this research is considered as applied research. The statistical population is the managers of three automobile industrial companies in Iran, including IranKhodro, Saipa, and ModiranKhodro. The statistical sample size of 36 people was selected by a combination of two methods Nonprobability sampling (Judgmental) and Snowball sampling. Using library and field studies, the factors and sub-factors of the research have been selected. After library studies, seventeen sub-criteria in three main criteria of attenuating, amplifier, and possibility were identified as a conceptual model. After Gaussian fuzzy, 125 rules are set, based on experts' opinions. According to the results, if the changes in the attenuating interval are more than 0.5, it will reduce the performance, and if it is less than 0.5, it will not have much effect on the performance. If the changes in the amplifier and possibility intervals are less than 0,5, it creates a moderate level of change in performance. And if greater than 0.5, it will increase performance output. To validate and ensure its efficiency, Extreme Condition test has been used. The results of test for three SUB-FIS and all the designed FIS, showed reasonable behavior towards the limit’s values of the inputs, which indicates the validity of the designed model and that means the system has good accuracy and validity of the evaluation.

One of the methods used to measure and evaluate employees is the assessment center. Assessment centers usually have good validity at the instrumental level, but have weaknesses at the summation level. This research seeks to provide a fuzzy system for estimating AC final scores based on competency, intelligence and personality scores by modeling mentality of experts in this field. For this purpose, the data of the assessment center and expert’s opinion have collected and finally a model has been presented to combine these three categories of concepts. The developed model has three sets of trapezoidal fuzzy numbers and 27 rules. In order to validate this model, the data of 116 managers of the country and the simulation of Monte Carlo were used, and it is clear that competence, intelligence and personality are the most important parameters, respectively, in the final score of the evaluation center

Earthquake is one of the most destructive natural disasters. Earthquakes occur in urban areas, destroying buildings and injuring people living in them. Urban center buildings are among the features that are exposed to many hazards during an earthquake. One of the first measures taken after an earthquake is relief. Locating damaged buildings can speed up relief efforts. One of the main sources in preparing the demolition of buildings is the science of remote sensing. Many researches have been done in the field of damage assessment using remote sensing images and the purpose of this research is to determine the extent of building destruction after an earthquake using plaidis data and fuzzy logic.

In this study, we used Plydez satellite images related to before and after the earthquake, in a period of less than one week after the earthquake. In this study, the first-order statistical tissue properties and the properties obtained from the matrix also were used. Then, using the genetic optimization algorithm, the optimal tissue properties were selected and using the isodita clustering and integration algorithm. Spectral information and optimal texture properties were determined for the roofs of the extracted buildings of three classes.

The condition of the buildings was done by a fuzzy inference system with Mamdani inference motor and fuzzy rules database designed using reference buildings and a total of 250 buildings were entered into the algorithm and classified into three classes of destruction.

The main output of this research was the demolition plan including three classes of healthy buildings to low degradation, high degradation and destruction. The use of reference buildings in estimating the accuracy of the proposed method showed an accuracy of 74% and acceptable method in determining the condition of buildings after the earthquake.

The present research is applied in terms of purpose and descriptive-survey method. Due to the research gap in the field of identifying talented people in previous researches, the present study has been compiled with the aim of identifying talented faculty members. For this purpose, two variables of Performance (Educational and Research) and Potential (Ability,& Aspirations and Engagement) of faculty members have been used. The statistical population of the present study is 350 faculty members of Mazandaran University who have been selected as a sample using purposive sampling method of 110 faculty members. Data related to educational performance have been prepared in cooperation with the Office of Supervision of Evaluation and Supervision, Quality Assurance and data related to research performance in cooperation with the Vice Chancellor for Research. To evaluate the ability of faculty members, the evaluation form of the CEB model(2005) and also to evaluate job aspirations and job engagement, two standard questionnaires, Nadi et al (2010) and Shuffle et al(2006) were used, respectively. The content validity of the questionnaires by experts and the reliability of the questionnaire is job aspirations (81.8) and job interaction(91.9). After initial clustering on the data, a fuzzy inference system with two inputs (performance and potential) and one output (Talent) is defined at five levels. The results show that most of the faculty members are 42.72% in the "intermediate" section. Also, 11.82% were in the "very low" section, 16.37% in the "low" section, 25.45% in the "high" section and 3.64% in the "very high" section

Corporate growth is an important factor for economic growth. Therefore, choosing the right growth strategy is one of the most important issues in managerial decisions. Accordingly, the purpose of this study was to design a business growth strategy selection model based on Ansoff matrix using fuzzy inference system. To achieve the research goal, first based on the ANSF matrix, Factors affecting market product strategy selection are identified and then two-level fuzzy inference system designed to select the company's growth strategy. The statistical population of this research was chemical and chemical industries of East Azarbaijan province. The statistical sample is based on the sample of available 124 managers of these companies. A questionnaire was used to collect the data, which was distributed among the sample members after determining its validity and reliability. In order to analyze the data while using exploratory factor analysis, a fuzzy inference system based on triangular membership functions and Mamdani inference is used to formulate the model of choosing the growth strategy of the company. The research results show that the designed system is able to identify the right growth strategy for companies. Also, the results in determining the growth strategy of the companies show that for the surveyed community, the best growth strategy was market development.

Given the important role of prevention managers in promoting organizational performance, recognizing the characteristics of these managers is of particular importance. The purpose of this study is to provide a model for identifying individual characteristics of NAJA's proactive managers and predicting their proactivity in uncertainty situations. This research in terms of research purpose, is developmental and, in terms of research design, is descriptive-survey. Also, the present study, in terms of the nature of data is quantitative. The statistical population of the study includes experts and directors of NAJA in Guilan province. Two questionnaires were used to collect data. Fuzzy inference system was used to design the model. The results of the fuzzy inference system also show that three important characteristics of the proactive managers include personality traits, internal motivation and knowledge, skill and ability to forge managers. Law enforcement officers pay attention to the above-mentioned features in order to identify, select, and appoint NAJA Interaction Managers, and personality traits should be considered more than others.

Maintenance and Repairment is important, because Efficient Management can be very Effective in Countinuity Production Lines and Reduce Costs. This Problem is very important in power plants as the main resource of energy supply in the country. The Purpose of This Paper is to Presentation a Practical Approach to Determine Maintenance Strategy that is Suitable for any Device. In This Regard, the Fuzzy FMEA Approach and Design Fuzzy Inference System Evaluated the Risk of Critical Failures; as well as the Intensity also was assessed for Futher Investigation; Combining these Results, Appropriate Maintenance Strategy each Device was Determined. The Results Show, the “Generator” as the main unit of Electricity Production in Power Plants, with the Risk Priority Number of 6.56 and Intensity of 0.55 in the Fourth Dimention of Graph RPN-TI has been. In This Case, the Appropriate Strategy for this Device, the Maintenance Strategy is Proactive. Since the Devices include: Ayrpryhytr, Turbine Condenser, F-D Fan and Electro-motor with RPN high and Low TI; in the Third Dimention of Graph have been, Strategy Condition- based on the Situation is the Appropriate Strategy for their Maintenance.

The widespread use of medical technology required a considerable amount of resources to be purchased for equipment. Obviously, if the process of purchasing is not properly managed the quantity and quality of these items will not be coordinated with the actual needs of the hospital. Therefore, to avoid wasting resources, money and time, the present article seeks to provide a fuzzy system to evaluate and prioritize the risks involved in purchasing of BUALI hospital. In the present study, a list of possible failures was first identified by studying the subject literature and interviewing experienced experts. Then by more interviewing and examining the experts, the most critical failures were extracted from the list of failures in terms of their importance and their impact. The RPN of each failure was calculated based on the FMEA approach in both classic and fuzzy ways. In fuzzy method, the fuzzy expert system was used to provide a failure risk assessment model. According to the results, the most important failures in the BUALI hospital purchase process, which should be considered more than once, are: "lack of marketable goods", "delayed delivery of goods" and "instant goods deficit". Applying the fuzzy inference system, despite the risk rating of the purchasing process, can provide a clear understanding of each of the risks due to the degree of membership associated with the fuzzy RPN. Therefore, considering the sensitivity of the purchasing process in hospitals, it can be considered for better shopping management.