جستجوی مقالات مرتبط با کلیدواژه « مجموعه های راف » در نشریات گروه « مدیریت »

Considering the importance of the issue of efficiency in the advancement of societies and the place it occupies in today's organizations, the use of performance evaluation has become an unavoidable necessity. In the present study, the performance evaluation of five active supply chains with the same structure with the aim of comparing the results of the combined RDEA-BSC and FDEA-BSC models in Tabriz automotive industry and the input and output data in the form of symmetrical triangular fuzzy numbers and the set of uneven numbers to the input and output model The models show the performance of the supply chain. From the Balanced Scorecard (BSC) method as a tool for designing performance evaluation indicators in four aspects; financial, processes, customer and learning and human force growth have been used and also the type of applied-descriptive research and measurement tool is questionnaire, financial documents and information analysis method, FDEA, RDEA, BSC mathematical model and sensitivity analysis. The results of the research show that the efficiency of Amico in each model is higher than other studied companies

In this research, by combining the methods of principal component analysis and the Rough sets, a model is proposed to predict stock prices. First, a number of technical indicators were calculated using the one-year price data of IranKhodro Company. In order to reduce the decision matrix dimension, using the principal component analysis method, new variables were selected so that the maximum characteristics of the initial data were maintained. These variables are used as conditional components in the decision matrix, and the decision variable is next day stock price fluctuation. The data were converted into discrete intervals by different methods and then divided into two groups of learning and control. Then, using the theory of Rough sets on learning data, the decision rules were extracted and their validity on the control data was evaluated. The results obtained from the combined model were compared with the results of the Rough sets model. The advantage of the Principal Components Analysis and Exploratory Factor Analysis methods is the ability to name new factors as the factor of the momentum and the moving average factor, which makes the results more tangible. The percentage of correct predictions of the rules extracted from the hybrid model is higher than the alternative model and the number of rules is lower. In order to verify the reliability of the model, the data of the period of 2002-2017 of IranKhodro Company and also the data of the Iran Saderat bank were studied. The results were consistent with the previous findings.

Rough set theory is a new mathematical approach to analyze the imperfect knowledge. It does not need any preliminary or additional information about data and provides efficient methods, algorithms and tools for finding hidden patterns in uncertain data. In this study, RST has been used to extract the rules from the data of energy audits of buildings. Since part of building energy audit data related to assessment of occupants comfort level and other data related to the technical analysis of the buildings so in this research, a decision attribute and eleven conditional attributes have been selected and rules inference have been done using ROSETTA software. Due to the different algorithms of data complement, discretization, reduction and rule generation, four rule models have been constructed based on the conditions of this study. Cross validation is used for evaluation of the model results. Finally the best model was chosen with fourteen rules and 99.8 percent of accuracy. The model demonstrate that the core attribute of buildings is "uncontrolled area of buildings". It means that if the value of this attribute is calculated the 14 rules can be used to accurately predict the level of employees comfort in the buildings.

Understanding and changing the energy consumption behavior requires extensive knowledge about the motives of behavior. In this research, Rough Set Theory is used to investigate the energy consumption behavior of employees in organizations. So, thirteen condition attributes and a decision attribute have been selected and the decision system has been created. Condition attributes include demographic, values, attitudes and organizational characteristics of employees and decision attribute relates to energy consumption behavior. 482 employees have been selected randomly from 37 office buildings of ministry of Petroleum and rough modeling has been performed for them. By combining different methods of discretizing, reduction algorithms and rule generating, nine models have been made using ROSETTA software. The results show that four of the 13 condition attributes, involving “organizational citizenship”, “satisfaction”, “attitude toward behavior” and “lighting control” have been selected as the main features of the system. After cross validation of the various models, the model of manually discretizing using genetic algorithms and ORR approach to extract reducts has been had the most accuracy and selected as the most reliable model.