جستجوی مقالات مرتبط با کلیدواژه « منطق فازی » در نشریات گروه « صنایع »

In this research, a hybrid approach based on fuzzy logic and artificial neural network is presented to predict the failure of machines in order to increase productivity. The subject of this research is one of the factories of the automobile industry named Diaco Ide Aria, which operates in the field of automobile parts production. Preventive maintenance requires correct prediction of breakdowns and accidents, equipment and machines so that productivity can be increased by timely and correct maintenance of machines as well as fixing defects and breakdowns. To model the multi-layer perceptron fuzzy-neural network (MLP), first, 100 failures and stops were collected in a period of 15 months and then entered into MATLAB software. The obtained results show that the implementation of fuzzy-neural network and the prediction of machine failure time has reduced the duration and cost of repairs. Therefore, the working time and accessibility of the machines increased and ultimately increased the productivity by 57%, also, the accuracy of the developed neural-fuzzy model was estimated at 94%.

Human reliability is an important issue that is evaluated in order to prevent catastrophic events in high technology industries. The purpose of this study was to evaluate human reliability by CREAM fuzzy method in short and medium range radar systems, which ultimately led to the study of human reliability and the probability of error in each of the effective factors. Then the method of hierarchical analysis, pairwise comparisons, calculation of weights and system compatibility, matrix of pairwise comparisons and control style, and ranking were performed. After collecting information from questionnaires and consulting experts, job analysis was performed by hierarchical analysis (HTA). Then the table of working conditions affecting the operator's performance (CPC) was drawn, and working conditions affecting the user's performance were presented using the table of CPCs. In the next step, the fuzzy AHP method was investigated , after calculating the pairwise comparison matrices, control styles were determined. At the end of the work, the factors causing the error along with the amount of human reliability and the probability of error in each factor were identified. The results indicated that human reliability is significantly improved. Based on the findings of the study, determining the contribution of various factors influencing human error, provide a more effective assessment. Among the job tasks, the factor of "preparation of mechanical parts" with a probability of 0.416 has the highest probability of error and should focus more on this factor.

Intelligent assistant systems have been focused in many researches for managing uncertainties in medical diagnosis in the recent years. Due to vagueness in diagnosis of Breast cancer stages which is one of the major cause of death in women, early diagnosis of stages of this cancer can help physicians to choose the best treatment option. In this research, an intelligentModel based on fuzzy logic has been presented to manage uncertainty associated with input and outputs in the diagnosis of stages of breast cancer. This model implements human experience with membership functions and fuzzy rules and is a general method for combining knowledge, intelligent technology, control and decision making. In this study, the medical records of 400 patients with breast cancer with 3 features were studied and their results were evaluated by a group of experts. The results of the system efficiency were investigated using an ROC curve analysis method. The specificity, sensitivity and accuracy in steps 1, 4 of the Breast cancer were (97/50%, 98/43% ), in order.

The purpose of this article is to design a GSNCL optimization in the dairy industry of Damdaran using FGP model.

This article is applied in terms of purpose and in terms of data type and how to implement descriptive-survey research and dissertation modeling, with a FGP approach and has optimized the GSNCL in Damdaran industry. The society and the statistical sample consisted of 15 experts in the dairy industry of Damdaran, who have at least 10 years of executive experience in these industries and evaluation of supply network, as well as academic professors specializing in the field of GSNCL.

The results of modeling and solving the numerical model, the importance of the role of financial dimension and simultaneous consideration of the environmental, financial and social dimensions in the mathematical model to gain competitive advantage of the GSNCL. Also, cost parameters have a positive effect on the economic performance of the main supply chain program, but by examining these parameters in sensitivity analysis, it causes a decrease in production performance in social and environmental dimensions.

Knowledge-enhancing research from an applied point of view can provide important information for decision makers, senior managers of dairy industry in the country about optimizing the GSNCL and maintaining the environment. The innovation aspect of the present study is to design a four-objective model to minimize economic costs, emissions of pollutant gases throughout the living network, the amount of social damages, and maximize the total revenue generated from the sale of products during the supply network using FGP. Modeling of FGP problem in a multilevel, multi-round and multi-product supply network has been carried out with the aim of minimizing undesirable deviations of each of the four ideals from the desired level.

Intelligent assistant systems have been focused in many researches for managing uncertainties in medical diagnosis in the recent years. Due to vagueness in diagnosis of Breast cancer stages which is one of the major cause of death in women, early diagnosis of stages of this cancer can help physicians to choose the best treatment option. In this research, an intelligentModel based on fuzzy logic has been presented to manage uncertainty associated with input and outputs in the diagnosis of stages of breast cancer. This model implements human experience with membership functions and fuzzy rules and is a general method for combining knowledge, intelligent technology, control and decision making. In this study, the medical records of 400 patients with breast cancer with 3 features were studied and their results were evaluated by a group of experts. The results of the system efficiency were investigated using an ROC curve analysis method. The specificity, sensitivity and accuracy in steps 1, 4 of the Breast cancer were (97/50%, 98/43% ), in order.

Whereas the productivity generated by implementing innovation in firms for competitive advantage and ultimately having a better performance is the result of the interaction between the business environment, innovation strategies, and corporate capabilities; And that so far, significant studies have not been performed on the interaction of the business environment (as an external factor) and innovation strategies on firm performance; Therefore, the main objective of this research was to investigate the impact of a firm's business performance by aligning innovation strategies (two dimensions of "product innovation" and "process innovation") with the business environment (within the three factors of "dynamism", "competitiveness", and "technological change").Using the data obtained from the questionnaire - from experts of Iranian plastics manufacturing companies - and finally modeling, and analyzing the data by the fuzzy inference system in MATLAB software using the method of Mamdani implication; The study shows that in this industry, the process innovation will have a greater impact on business performance than product innovation. furthermore, technological changes have a greater impact on business performance than dynamism and competitiveness. Overall, this study illustrated the strategic alignment between technological change and process innovation strategy in improving business performance. These results necessitate the targeted selection of innovation strategy based on different environmental conditions for firms.

The models of Data Envelopment Analysis (DEA) based on Goal Programming (GDEA) seeks to address some drawbacks of classical DEA by increasing the degree of resolution and providing real weights to Decision-Making Units (DMUs). Experimental results indicate that the GDEA models do not completely cope with these in some cases which are tested. Also, in calculating the optimal solution with different methods of evaluating the efficiency of units, we are faced with a group of Pareto optimal solutions that make a decision maker facing a serious challenge in choosing the most appropriate solution. To solve this, in the first step, this paper uses the concepts of fuzzy logic and then proposes the F-GDEA approach based on fuzzy logic in solving the GDEA models, which increases the resolution of the methods to rank the units. In the second step, by using the F-GDEA approach, we propose a new hybridized fuzzy approach called HF-GDEA for short, taking into account the various ranking results from the different programming models. With this new proposed approach, we combine the rankings obtained from different methods and present a new ranking for the DMUs. In other words, the HF-GDEA approach makes it possible to compare and thus select an optimal solution from Pareto's optimal solutions set. Finally, the proposed approach is applied to two practical examples and their numerical results are presented.

Nowadays, choosing the right strategy is one of the most important factors in promoting and preventing the loss of company capital. Achieving an integrated system that takes into account the conditions for selecting different strategies, in considering all the effective variables and key factors, is essential for any company. In this paper, using previous studies in the field of technology strategies and corporate behavior, we have attempted to develop a model to provide appropriate technology strategy choices based on key effective components such as appropriability, opportunity, accumulation and implication of knowledge and by integrating technology strategies and corporate behavior. The results of the examination of the balance between basic technologies and Freeman's technology strategies, in a table named of concordance table are extracted and categorized and integrated on the basis of common components with the corporate behavior table. The resulting integrated table, which includes different modes of business environment, is used as the external factor of the company and the absorption coefficient and leakage coefficient are considered as internal factors in the Fuzzy analysis in the FIS system and accordingly the fuzzy model of choosing the appropriate technology strategy of the company is developed. Finally, it is validated using a numerical example of the proposed model.

Intelligent assistant systems have been focused in many researches for managing uncertainties in medical diagnosis in the recent years. Due to vagueness in diagnosis of Breast cancer stages which is one of the major cause of death in women, early diagnosis of stages of this cancer can help physicians to choose the best treatment option. In this research, an intelligentModel based on fuzzy logic has been presented to manage uncertainty associated with input and outputs in the diagnosis of stages of breast cancer. This model implements human experience with membership functions and fuzzy rules and is a general method for combining knowledge, intelligent technology, control and decision making. In this study, the medical records of 400 patients with breast cancer with 3 features were studied and their results were evaluated by a group of experts. The results of the system efficiency were investigated using an ROC curve analysis method. The specificity, sensitivity and accuracy in steps 1, 4 of the Breast cancer were (97/50%, 98/43% ), in order.

Fars Regional Electric Company is one of 16 regional energy companies in the country, which is responsible for managing power transmission in the provinces of Fars and Bushehr. The company uses a variety of management systems to promote its offering of services, which is one of these systems Suggestion System. In accordance with the executive procedure of the admission, examination and implementation system of the Suggestion System, each stage of acceptance and implementation of the Suggestion is also intended to reward the position of thought and thought. The reward of each offer is proportional to the score that it receives after approval by its specialized committee. In order to standardize the scoring for each suggestion, indicators and criteria are also included in the company's method of implementation, which is categorized in three areas of technical, non-technical and financial criteria. Given the lack of tools for calculating indices, each index must first be described and then converted into a number as a score. In this paper, while introducing the Fars Regional Electric Company, the position of the Suggestion System and the current scoring method for any Suggestion in this company, using a fuzzy inference, Using the various criteria of each Suggestion, system suggest a total score for each suggestion is close to expert opinion. The results of using this system indicate that the decision quality in the allocation of score to each Suggestion is increased and the rules in the system fuzzy inference engine provide a better score.

The human resources employed by organizations are the main source of funds for managers, and HRM is the most important and perhaps most important task of managers. Behavior and activity of humans is due to their motives or needs. The issue of allocating fair rewards to those who have a motivational aspect has always been a concern for the managers of the organization. In this paper, we have tried to provide a model for calculating motivational payments to employees. Given the nature of the uncertainty of individuals in organizations, this research uses a fuzzy logic method to provide a methodology to address this issue. It is worth noting that the resulting model is presented to evaluate its effectiveness in a training organization and its results. Due to the obvious capabilities of the fuzzy method in helping to make decisions with uncertainty, this method will be used in the calculations of this model. The present model has been used for the number of 23 people in the organization and the motivational reward has been calculated per person. The results indicate that executives are fair and satisfied with the amount of rewards allocated to employees

Today, given the changing circumstances, in order to maintain their competitive forces strong, successful enterprises should adapt themselves to the turbulent and changing conditions in the business environment. To achieve this, supply chain agility has been taken into consideration as an essential component for improving competitiveness. Thus, this study aimed at assessing the agility of supply chain in the ceramic tile industry, as one of the most important industries in Iran. Using a descriptive analytical study design and quantitative techniques, this study was conducted as a desk research and followed a practical application. The ceramic tile companies of Yazd, Iran made up the statistical population of this research. The statistical data were based on the effective criteria and sub-criteria of agile supply chain, which by fuzzy analytical hierarchy process (FAHP) method, weighting, and then were analyzed and ranked using the combined model of FAHP and VIKOR, with the help of some macro software such as Super-Decision and Excel. The results of the study showed that mahceram ceramic tile company was ranked first in terms of supply chain agility, followed by EEFA Ceram and Aghigh companies, respectively. Furthermore, regarding the importance of the criteria, product development flexibility held the highest importance with an importance coefficient of 0.307, while manufacturing flexibility had the lowest importance with an importance coefficient of 0.106.

One of the problems of based models on data envelopment analysis (DEA) for ranking preference voting systems is that it allows each alternative have its own weight vector. Therefore, alternatives are evaluated with different weight vectors. In this study, we propose a model based on fuzzy logic to solve the weaknesses of the previous models. This model is based on the solving of multi-objective programming models with the help of fuzzy logic, by this way it providing a vector of common weights, and finally, we can rank the alternatives .

The cost of design and building industrial systems is greatly affected by determining the exact number of machineries and human resources, consequently allowing to achieve a higher level of efficiency and productivity. Different methods have been presented to estimate the number of required resources for operational processes. The reliability of the results from these methods is highly dependent on the estimation of the input data which, under uncertain conditions, might have a vague nature and convey incorrect information. Therefore, this study aimed to propose a novel framework based on the fuzzy logic to determine the optimal number of machineries and human resources. The fuzzy set theory was used to determine the percentage of wastes and the time required to complete operational processes. Moreover, to prove the practicality of the proposed model and given the significance of improving the productivity of Textile industry in Iran, the proposed model was employed in a case study of the textile industry and the results were compared with the standard method. The results suggest a significant difference between the number of machineries and human resources estimated by the proposed method and that by the standard method. These differences may negatively affect the performance and optimal usage of the available capacity. The results obtained from the proposed model offer more accurate and comprehensive information under uncertain conditions, allowing us to make appropriate decisions to revise the unused capacity, reduce the cost of idle resources, and increase the efficiency and productivity of the industry.

Correct selection of projects The first step is project-based organizations in the targeted management of project portfolios. This is a complex process selection that includes many factors and considerations. Market conditions, global rapid changes in various dimensions and other related issues in the real environment have increased the uncertainty and ignorance of these issues. It is therefore necessary to provide models for showing the real status of the organization and its goals and preferences. In this paper, the goal is to provide a fuzzy fuzzy multi-objective model for the portfolio of rail transport projects considering the uncertainties in variables; budget, time needed to complete the project, environmental pollution, risk, and quality. In this model, minimizing environmental pollution, maximizing quality, minimizing the risk and cost of projects under inflation is considered in the objectives of the problem. Due to the fact that the model was presented, a particle swarm algorithm was used to solve the problem, and finally, the results were compared with the genetic algorithm in order to measure the efficiency.

Nowadays, along with the constant increase of using cloud environment by companies and organizations, scheduling jobs in this environment in an optimum way is of prime importance. Therefore, different algorithms have been suggested for assigning tasks to resources in cloud environments; however, most of which do not consider criteria such as balanced load, and reduction of the task completion time. In this work, using the meta-heuristic algorithm of swarm particles optimization (PSO) and fuzzy logic, task completion time is reduced, and, as a result of which, efficiency of using resources is increased. Generally, in a distributed system like cloud environment, tasks are assigned randomly to resources. Hence, total load on the cloud environment could become imbalanced, which reduces system’s efficiency. In this research, PSO and fuzzy logic is used for job scheduling. In addition, the use of simulated annealing (SA) to improve the initial solutions, which are generated randomly, is suggested. Results show that the suggested optimization method can effectively improve criteria like makespan once compared with results of algorithms without optimization, like Ron-robin, and even in comparison to other optimization algorithms, like genetic algorithm.

One of important issues in designing a supply chain is supplier selection. The issue is complicated because each of suppliers should met some criteria from buyer and choosing among them is actually a MCDM problem. So there is a need for a structured and systematic approach to solve this problem. In this article we address supplier selection in advertising industry and two new criteria are proposed. Also a new approach combining the techniques of MDL and fuzzy Topsis is proposed to evaluate suppliers in the industry.