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

This article investigates the selection of a cooperation model with suppliers and the continuous monitoring of the collaboration strategy over time by tracking the supplier's performance. A multiple fuzzy profile monitoring model is proposed and implemented in two stages to oversee the supplier’s unforeseen costs. In the first stage, criteria and indicators for evaluating the delivery process and quality are identified based on literature reviews and expert opinions from industry leaders. In the second stage, a two-phase monitoring approach is adopted. First, using the T² method, the model’s parameters based on successive differences are calculated and separately monitored for the multiple fuzzy profiles of delivery and quality processes. In the second phase, the likelihood ratio method is applied to track the profiles over time, enabling the control chart to signal any warning in the shortest possible time. In the final stage, the results from the fuzzy profiles of delivery and quality processes, combined with artificial intelligence and the fuzzy inference system tool, are used to monitor unforeseen costs, make decisions regarding the supplier, and assess the adopted strategy. This model has been implemented in Iran's automotive industry, specifically within Iran Khodro Company and its gearbox parts supplier, Niromoharkeh Company.

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.

Decision-making in issues such as lean production is highly complex. On the one hand, this complexity is due to the existence of linguistic terms, and on the other hand, decision-making in issues such as lean production depends on the knowledge of experts. Considering that lean measurement is the first step in recognizing the strengths and weaknesses of industries in order to achieve the goals of lean production, therefore, it is necessary to develop more practical methods in this field. Based on this, the aim of this research is to provide a model for evaluating lean production using fuzzy inference systems. The presented model consists of five fuzzy inference systems designed in two levels. The four first level systems, as effective tools on lean production, produce a score as output, and these outputs are used as input to the final system to evaluate lean production. Triangular membership functions that were chosen for efficiency and ease of use have been used in the design of each of the fuzzy inference systems. The results of developing the model and using it in a machinery and equipment company show that the presented model, while showing the state of lean production in the organization, is able to determine the state of the organization in each of the factors and criteria affecting lean production. also specify

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.

In the last few decades, a decreasing trend is observed in livability of cities due to growth of technology and applying the modernity concept in the construction of cities, which causes the adverse effects on urban spaces. One of the most prominent of these effects, which can be called urban damage, is the decline of values associated with the sense of place in the formation of these spaces. This concern is especially highlighted in metropolises where technology has naturally grown faster. This study provides a framework to estimate the livability in Tehran metropolitan district 1 by considering the sense of place factors. In this way, a novel predictive model is developed based on the integration of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA). To this end, several variables based on the sense of place including physical body, meaning, activity, social capital, sense of place, and mental quality of life are considered as predictive variables. Results indicate that the ANFIS-GA model (R=0.926) is the most accurate predictive model when all input parameters are included. Besides, the social capital index has very little effect on livability and the importance of mental quality of life index is more significantly than other indices and can lead to more improvements in livability of regions than other factors.

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.

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.

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.

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.

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

This study aims at presenting a model for evaluating the effectiveness of implementing the Five-Year Development Plan Policies of the Islamic Republic of Iran in the Social Security Organization. As a combined method, quantitative methods (fuzzy inference system) and qualitative methods (interviews to determine fuzzy rules) have been used in different stages so that the implemented policies could be adequately evaluated. The statistical population of the study included the managers of the Social Security Organization and university professors who were selected through the purposive sampling method. The theoretical framework used to evaluate the effectiveness of the policies implemented in this organization was a combination of the two models of Matland and Hill and Hope. The results of the study showed that the level of conflict and ambiguity in the policies of the Social Security Organization was relatively high and the highest weaknesses were in terms of evaluation and accuracy in formulating the policies in this organization. The results of the dependence between the variables and the if-then rules indicate that if there are so many contradictions and ambiguities in the policies, if the level of using influence tools (use of force in the implementation of the policy), exchanging (use of bargaining and market-oriented view), and persuasion (cultural mechanisms) is too much, the policies will get ineffective. However, the final output of the Matland model indicated that due to the fuzzy nature of the model, the policies stood between the two functions of high and medium membership which shows low level of effectiveness.