MODERN RESEARCHES IN DECISION MAKING
Year:2022 | Volume:7 | Issue:2|Papers Count:10

Integrating production scheduling and scheduling is one of the most important decisions in any production organization that leads to increase the effectiveness of operations management. Long-term production planning decisions (determination of production quantity, inventory level, etc. ) and short-term operational scheduling decisions (order of work processing and maintenance activities) have a direct effect on each other. In this study, a new mathematical model for simultaneous decision making of production planning and scheduling is presented with the aim of minimizing the total costs, time and delay in the hybrid flowshop. The concept of imperfect maintenance and considering several types of repair maintenance sources is presented in the proposed model. Two multi-objective meta-heuristic evolutionary algorithms based on the harmony search algorithm and the exact Epsilon-constraint solution method are proposed to solve the proposed model, which continuously searches the decision-making solution space. For this purpose, a sample of test problems of literature review and three performance criteria of Pareto number of solutions, mean ideal distance and Spacing metric are used to compare the results of meta-heuristic algorithms. The results show the advantages and effectiveness of the methods used in determining the Pareto optimal solution for optimization problems.

In recent decades, the topic of performance measurement has been one of the popular topics for large companies and manufacturers managers. The range-adjusted measurement ( RAM ) model in data envelopment analysis (DEA) is a non-radial model used to evaluate the performance of firms. Due to the presence of uncertain data in many investigations, a fully fuzzy range-adjusted measurement model with strong complementary slackness conditions to find efficient two-stage systems in a reference set is presented in this paper and it is used to evaluate airline. Given that we have a multi-objective network model of fully fuzzy range-adjusted measurement with strong complementary slackness conditions, the proposed model is solved using the lexicograph method. We also compare it with the existing fully fuzzy network DEA-range adjusted measurement model. Finally, we apply this model using the data of 14 Iranian airlines.

This study evaluates the performance of Iran and 38 OECD countries by providing a DFM model with undesirable outputs. Comparing the changes in the rate of increase in output and the rate of decrease in inputs for the efficiency of decision units in DFM and CCR models is one of the objectives of this study. This research is applied research in terms of purpose and is descriptive research in terms of implementation. According to the review of the literature and research background of the employed Total labor force, Total primary energy consumption and Gross Capital Formation were considered as input, GDP was considered as a desirable output and total GHG emissions excluding LULUCF, was considered as an undesirable output. The results showed that inefficient countries should make the most change in the input of total primary energy consumption and the least change in the desirable output (GDP) in order to reach the efficiency frontier,therefore, considering energy consumption management can be effective in changing inefficient countries with poor performance in environmental sustainability, such as Iran into efficient ones. The analysis also showed that in order to improve the sustainability in Iran, policymakers should consider reducing energy consumption, increasing labor productivity, reducing greenhouse gas emissions and improving investment productivity and GDP. According to the research method, two modeling methods of evaluating the efficiency of countries in the field of sustainable development were compared and the advantages of DFM model in comparison to CCR were studied.

Decision making is one of the most important aspects of each person's individual and social life. Understanding consumer decision-making is an important issue for various groups such as policymakers, managers, producers and marketers. The present age is the period of growth and development of cognitive and behavioral sciences. At present, explaining consumer behavior with the approach of cognitive and behavioral sciences has a large amount of information that it is important to conduct combined research that systematically and scientifically presents the information extract to researchers. The aim of this study was to enumerate the components affecting decision-making based on cognitive and behavioral sciences. The present study is a descriptive research and in terms of purpose is a basic research. This research has analyzed 130 numbers of researches conducted between 1959 and 2021 by using the meta-synthesis method and using Maxqda 2020 software and Excel software. The statistical population included all valid domestic and foreign researches available in the databases available to the researcher. The final analysis of the data has led to the inference of a related set of concepts, categories and subcategories. The result of this research included 3 themes and 16 effective categories in decision making that formed the elements of the model. The most frequent components influencing consumer decision, consumer goals in decision making, types of consumer behavior and types of consumer decision models are also explained in the results section. This research has innovations in method and content in the field of consumer behavior.

With the development of mobile communications and the advent of the Fifth Generation, increasing Internet speed and capabilities as enablers of the Internet of Things is creating changes in people's daily lives and work. On the other hand, there are environmental issues, the possibility of pathogenicity, and doubts about the realization of the imagined features, which are much debated today. Most researches has paid attention to technical and developmental dimensions of it, while from the social science perspective, the acceptance of new technology and from the marketing perspective, consumer satisfaction can be important for service providers. Accordingly, the purpose of this study is to investigate the perception of Twitter social network users as a microblogging platform about the fifth generation of mobile communication using machine learning methods and Sentiment Analysis. Therefore, a collection of more than 40, 000 tweets on this topic was compiled through the Twitter user interface and the Recurrent Neural Network model was created with79% accuracy. Finally, the topic modeling was done by LDA method for further deepening. The results shows that although there is dissatisfaction with the quality provided, the cost and coverage of the fifth generation of mobile communications, health concerns, rumors that Covid19 is related to the fifth generation, but still more people are optimistic about the future of this technology in various fields such as the Internet of Things and artificial intelligence.

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.

Today, customer attraction ability has come to be known as a competitive advantage in different organizations. Taking this into account, organizations must deal with the problem of demand reduction by adopting a strategy that enables them to retain existing customers and attract new ones. Therefore, in the present study, the principles and components of system dynamics were used to investigate the performance of Sepah Bank in this field. Employment of system dynamic modeling for customer attraction purposes in a financial institution is one of the innovative aspects of the study. 11 main indicators of organizational performance extracted from the research literature were used for this purpose. In the presented model, the structural relationship and the significance of each variable were determined by experts. Afterwards, the system dynamics concepts were used to present (in causal loop diagram) the effect of these factors on the rate of customer attraction through banks and advertisement. In order to account for the functions of the proposed model, the researcher then examined the variables in three scenarios to determine the effectiveness of each under different conditions. Finally, the proposed scenarios showed that using the optimal scenario, one can improve customer attraction rate by 20% through the pre-determined model relations. The results of the study can be used toformulate customer attraction strategies in Sepah Bank.

One main problem in organizations is the lack of timely detection of large-scale changes such as crises. Detecting the changes is always delayed. One way to detect a crisis in an organization is to examine and analyze the social network of the employees. The models used so far in this field are generally graph-based and use the assumption of heterogeneity of nodes. Such models are not capable of modeling dynamic behaviors due to the static nature of the graphs. So far, no study has been found on the system dynamics approach in analyzing the behavior of longitudinal social networks. In order to fill this research gap, in this paper, using the system dynamics framework, dynamic social network modeling is done by considering the heterogeneity behavior of nodes. In this method, stochastic differential equation is used to simulate the collective behavior of nodes and to highlight the stochasticity of dynamic social networks. This modeling is simulated in Vensim software. In order to validate the proposed model, this modeling has been evaluated on Enron's communication network. Then, by examining the simulation scenarios and variables, it was found that the volatility rate or standard deviation of the data is a key variable in social networks. The results of this study provide a model of social media behavior in different situations for decision makers and planners.

In the last four decades, multi-attribute decision-making has always been an active field of research and application. So far, the focus has been developing or applying multi-attribute decision-making techniques to rank existing options. However, multi-attribute decision-making is not limited to this type of problem and also includes sorting problems. This study proposes a hybrid approach of linear goal programming-based best-worst and multi-attribute sorting methods to form a stock portfolio in the Tehran Stock Exchange. In this study, the weights of the attributes affecting the stock portfolio selection were determined using the linear goal programming-based best-worst method. Then, the stock portfolio was formed using the proposed multi-attribute sorting method. The proposed method can consider decision-maker preferences such as the range of stocks in the portfolio or the maximum number of stocks in each industry. The results obtained from the proposed method were compared with TOPSIS-Sort and VIKOR-Sort methods. The results showed the high accuracy of the proposed method, and the stock portfolio formed by the proposed method was significantly more profitable than other methods. The proposed approach in this research can be applied to different real-world sorting problems.