فهرست مطالب amir gholam abri

In the present age, evaluating and then improving the performance of production systems and supply chains as a whole is the only possible way to compete in the global business market. Data Envelopment Analysis (DEA) is a non-parametric method for measuring the relative efficiency of production systems and decision-making units with multiple homogeneous inputs and outputs as a mathematical programming method. Determining the type of return to scale (RTS) helps managers make more accurate predictions about the expansion or limitation of the decision-making unit (DMU). Due to the importance of RTS in relation to management decisions, various methods have been proposed by DEA researchers to introduce and determine its type. Research in RTS evaluation has led to a more general classification of RTS types, with the titles Left return to scale (L-RTS) and Right return to scale (R-RTS). This research studies the two-stage production process and introduces an approach to estimating the type of efficiency on the right and left scales of DMUs. In this paper, the application of the DEA network model to calculate the relative efficiency and return to scale of cement companies listed on the stock exchange has been studied. The data used in the model is related to the production process of 42 cement companies in 2020.

Examining the relationship between income distribution and wealth distribution in Iran is very important in terms of economic and social consequences. Therefore, examining the Short-term and long-term inequality of income distribution and wealth distribution in Iran can provide some answers to the questions of policymakers and economic analysts. Focusing on Iran, this research has been conducted based on Piketty's dynamic model of wealth inequality in the time period of 2002-2019. The results showed that in the short term, different assets grow at different rates, and since the ability to earn and save income of the upper deciles is more than the lower deciles, it is expected that the distribution of income will become more unequal and lead to the inequality of wealth distribution. It is also predicted that in the long term, in a stable state, if the return on capital is zero, the advantage of the assets will be lost and will grow with the inflation rate. But what is happening in reality is that the process of distribution of income and share of wealth is in favor of the top 10% of the income decile and the distribution of wealth has become more unequal.

The policies used by governments in emerging and developing economies during the financial transition have always faced the question of what impact the central bank’s interventionist monetary and currency policies have had on banks’ risk-taking. In this research, the impact of monetary and foreign exchange policy on the risk-taking of commercial banks was investigated, and in this way, a sample consisting of 15 stock-exchange banks from 1386 to 1400 was investigated using the quintile regression method and the state space method. The estimation results showed that monetary and currency policies have a positive relationship with liquidity risk. In addition, the findings showed that with the beginning of sanctions, the liquidity risk of banks has increased and the impact of monetary and currency policies on larger banks has increased. Therefore, small banks are less likely to go bankrupt compared to large banks.

The aim of the current research was to provide an intelligent model of the green supply chain of pharmaceutical products with the overlap of common customers. This research applies simulation to the proposed model of a pharmaceutical supply chain with smart and green conditions. The society under investigation is the environment of pharmaceutical companies, with its specific assumptions and goals. This model is simulated in the GMAS software environment. The problem was examined from two perspectives: simultaneous production (coordination of drugs) and cooperative planning (coordination of suppliers). Additionally, in order to address the issue of vehicle routing under real conditions of limited capacity for delivery vehicles, the expiration date of the drug and time windows in orders were taken into consideration. The objective was to allocate orders to vehicles in a way that minimizes the total delivery time and reduces the amount of carbon dioxide produced. Based on the obtained results, this multi-objective model aims to improve the performance of the pharmaceutical distribution network by addressing three main complexities: economic, environmental, and social. This approach provides a comprehensive and balanced solution for designing the drug distribution network. Its goal is to preserve the environment, improve social conditions, and maximize economic profit.

The growth of "total productivity of production factors (TFP)" of the housing sector during the period of ‏2021-1986‏‏‏‏‏‏ , based on the techniques of "econometrics" and "envelopment analysis of data", into four components; Technological progress, change in technical efficiency, scale effects and specific efficiency were analyzed. The average value of these components respectively is 1.47 ; 0.53 ; 1.52; 0.15% and the average TFP growth is 3.67%. In this connection, technical efficiency, effective tensions and income share of production factors have also been calculated; The results show that the housing sector works close to technical efficiency and the trend of inefficiency is decreasing, the average effective elasticity of labor force and capital stock are 1.06 and 0.92 respectively, and the income share of these two respectively is 0.51 and 0.49 percent. The return to scale is also greater than one and increasing. However, production units are not able to improve the share of technical efficiency change in TFP growth. The average of other drivers is also positive, but due to the lack of synergy between technological progress and scale effects, and despite the smallness of allocation efficiency; have not had an acceptable impact on productivity growth.

Examining the risk-taking of banks as one of the pillars of the economy of any country is very important in facing monetary policy changes. In this paper, the impact of monetary and exchange policies on the default risk of banks has been investigated using quantile and state space methods, and in this regard, a sample consisting of 15 Iranian banks from 2006 to 2021 has been studied. The results indicate that monetary and exchange policies have a positive and significant relationship with default risk. In addition, during the first sanction period between 2011 and 2014, the effect of monetary policy on the risk of default increased, but after that, with the signing of the Joint Comprehensive Plan of Action, the coefficient of monetary policy decreased. Since 2017 and with the beginning of the second period of sanctions, the impact of monetary policy has increased greatly and has reached its highest level by 2021, that is, due to the adoption of expansionary monetary policy, the default risk of banks has increased, which is approximately It is twice as much as in the years before the embargo.

The purpose of this research was to design the model of organizational virtue along with identifying and prioritizing dimensions, components and indicators and validating the proposed model of organizational virtue with a qualitative approach in the Ministry of Keshavarzi Jahad. The method of conducting the present research is a mixed method (qualitative-quantitative) and the statistical population was 12 experts who were answered using the interview tool. In this research, in order to analyze the data in the qualitative part of the method Theme analysis (theme analysis) was used, and based on the results, the research pattern according to the output of Atlas T software, including 1 overarching theme (organizational virtue model with a qualitative approach in the Ministry of Jihad Agriculture) and 7 organizing themes (health, responsibility, trust, forgiveness, justice-oriented, cohesion, opening horizons) and 24 basic themes (financial health, moral health, work health; social responsibility, economic responsibility, legal responsibility, environmental responsibility; honesty, transparency, goodwill , competence; sympathy, sacrifice, kindness; justice in the establishment of laws, justice in the implementation of laws, justice in relationships between people; harmony, adaptability, solidarity; optimism, fulfillment of obligations, happiness, growth.

Competition in international markets requires the organization to conduct a competition that improves the atmosphere. These improvements do not only benefit the company, but also have a direct impact on the competitiveness of an industry as a whole. Organizations pursue new strategic advantages non-stop, so in this way they surpass their competitors. Innovation is an important and vital factor for organizations in order to create value and sustainable competitive advantages in today's complex and changing environment. In this regard, the purpose of this research is to investigate onto the impact of competitive strategies such as cost and differentiation on innovation and performance of small and medium industries.

The current research method is in terms of applied purpose and descriptive. The statistical population of the current research included managers and experts of small and medium industries, to determine the required sample size, Cochran's formula was used for the limited population, and the minimum required sample size in this research is 228 people, and people were asked to the Bayraktar questionnaire (2016). The validity of this questionnaire was confirmed by the critical factor analysis test with the help of Cronbach's alpha test.

SPSS and LISREL version software was used for calculations.

Examining the results of the research with the help of structural tests showed that cost, leadership, and strategy have no positive effect on performance; differentiation strategy has a positive effect on company performance with a coefficient of 2.71, leadership cost strategy with a coefficient of 5.22 and differentiation strategy with a coefficient of 3.85 on innovation. It has a positive effect and also the innovation strategy has a positive effect on the company's performance with a coefficient of 3.11.

The main drawbacks that arise for data envelopment analysis (DEA) are: lack of discriminationpower amongst efficient decision making units (DMUs) and scattering input-output weights. In theDEA, sometimes the mismatch of the input or output weights in the decision-making units (DMUs)under consideration leads to assigning higher weight to variables with the less significance and/or thelower or zero weight to the variables with high significance. Accordingly, most DEA models introducemore than one efficient DMU in evaluating the relative efficiency of decision-making units. The presentpaper is conducted to overcome these inabilities. In this trends, we present a novel DEA model basedon minimizing the sum of absolute deviations of all input-output weights from each other. The proposedmodel provides to enhance the discrimination power and adjusts the balance dispersion of input-outputweights. Finally, well-known numerical experiments are considered to demonstrate the efficiency andvalidation of the suggested model.

Traditional hyper-performance models in data envelopment analysis need detailed information about inputs and outputs. However, in many real-world problems this simple assumption does not hold. Its super-efficient stochastic models are one of the methods used to investigate the impact of uncertainty in data. One of the important assumptions in the method of random models is the certainty of the probability distribution function of imprecise data, which is hardly available in real world problems. In fact, in many problems, there is very little information from the data of the problem, which makes it impossible to estimate the probability distribution. The purpose of this evaluation is to analyze the sensitivity analysis of these academic centers when the data is imprecise.

In this research, by using the robust optimization approach, a hyper-efficient model is developed that calculates the uncertainty in the data in a multifaceted manner. Also, in the following, for the first time, the assumption of convexity was not considered in the super-efficiency model, and the stable model of super-efficiency was presented with the assumption of non-convexity.

If a unit under evaluation at one level of uncertainty has a high rating compared to other units, it will also have a high rating at other levels of uncertainty. It can also be said that more reliable results are obtained by considering the uncertainty in the data and analyzing the sensitivity of the models.

The current research seeks to provide a stock return prediction model using micro, market level and macroeconomic data.

The current research is applied in terms of research methodology. Bayesian averaging model and TVP_FAVAR were estimated in MATLAB 2021 software environment. The time frame of the research includes the period of 2011 to 2021.

According to the output, variables at the micro level, market level and macro economy affect this index; Also, based on the results of the TVPFAVAR model, it was observed that the effect of the effective variables on stock returns is generally positive and strong, and this effect is generally stronger in the long term than in the short term.

Originality / Value:

 64 variables affecting stock returns in three groups at the micro, market and macroeconomic levels were entered into the model, and then using the Bayesian averaging model approach, 11 non-fragile variables affecting stock returns, which are current ratio, ratio debt, rate of return on equity; Price-profit ratio, oil income, GDP growth fluctuation, informal market exchange rate, inflation fluctuation, money multiplier, interest rate and systematic risk were identified. For this purpose, to solve the problem of traditional models that do not have the ability to identify the most important variables affecting stock returns, the Bayesian averaging method and the generalized vector autoregression method of the time variable parameter have been used.

Supply chain is a complex network that includes products, services, and information flows between suppliers and customers, all of which create added value in the economy. The lesson of not operating this chain will be an obstacle to creating added value in the macroeconomic perspective. In addition to the above, the marketing, engineering and design units of a manufacturer are also part of the supply chain. In general, the supply chain is a chain that includes all activities related to the flow of goods and the conversion of materials, from the stage of preparation of the raw material to the stage of delivery of the final product to the consumer. The sole purpose of the supply chain is to minimize the total cost of the given network, To achieve this goal there is a need for a suitable business model in the industry. In this research, we propose the method of setting the optimal order quantity by optimizing particle swarm for supply chain management. In studies and after the implementation of the algorithm, we conclude that this algorithm has a high execution time in long distances and in high repetition cannot reduce the total cost, but in short distances is very efficient, the execution time is low. The total cost is low.

The purpose of the research is to predict stock returns using Bayesian averaging and BVAR. The current research is based on the applied research method and MATLAB 2021 and EVIEWS12 have been used to estimate the model. The time period of the research includes the years 2010 to 2019. First, 11 non-fragile variables out of 64 entered variables were identified with the Bayesian averaging model approach. Based on the results of the current ratio; ROE; P/E; oil revenue; The increasing coefficient of money in the whole period has a positive effect and inflation fluctuation variables; debt ratio; fluctuation of GDP growth; unofficial market exchange rate; Interest rate and systematic risk have a negative effect on yield in the whole period. Based on the results of variance analysis, the most explanatory of changes in stock returns is caused by the variable itself (20 percent), followed by interest rate variables (14 percent); Inflation volatility (13 percent) and debt ratio and systematic risk (10 percent) have the highest effect in explaining yield changes.

In this study, it was found that the degree of the industry complexity is related to the industry inefficiency degree and in this way affects the comparative advantage. For this purpose, factory industry data from twelve countries, including six developed countries and six less developed countries for the period 2011 to 2018 were used. By using the panel data method and the fixed effects technique, the impact of industry complexity on the degree of inefficiency was estimated for a set of selected developed and less developed countries. To measure economic complexity to the separation of industry from the average economic complexity of goods in that industry which is on the Harvard Atlas of Economic Complexity website was used. The data envelopment analysis method has been used to estimate the inefficiency degree index in each industry. The results of the research showed that the degree of inefficiency of the industry in developed countries increases with the increase of the complexity of the industry up to the threshold of 0.2 and then decreases. While in less developed countries, the inefficiency degree decreased with increasing industry complexity to the -0.69 threshold and then increased. In addition, the findings showed that less developed countries have a comparative advantage in industries with complexity in the range of -1.1 to 0.48. The comparative advantage in developed countries is in the range of 0.48 to 1.1. The final result is that less developed countries do not have the capacity to produce complex goods due to high inefficiency degrees.

This paper deals with modeling a nurse scheduling problem considering service level in uncertain conditions. In accord with the urgent need of hospitals to provide better services to patients, it is necessary to consider nurses’ preferences in the work shift scheduling. Hence, a multi-objective model is presented by regarding the rules and regulations related to the assignment of nurses to work shifts, in which the service level to patients is also considered. Due to the uncertainty of the number of patients that refer to the hospital, this parameter is taken uncertain into account. In order to evaluate the output results, two numerical examples in small and large sizes with real data of Labbafinejad Hospital with 18-person and 90-person wards are designed and Epsilon constraint method is used to solve the small-sized problem. Computational results reveal that increasing the service level to patients is directly related to increasing the number of nurses per each shift. Moreover, because of NP-Hard nature of nurse scheduling problem, the large-sized problem (90-person ward) is solved by a Gray Wolf algorithm and on the basis of designing a new chromosome. The results obtained by this method include 35 different efficient solutions for nurse scheduling in this hospital.

This research uses Network Data EnvelopmentAanalysis (NDEA) by  undesirable factors to analyze and evaluate the performance of automotive industry. The modeling used is applied to five production lines of an automobile company by 16 indicators. The data used are for the year 2019. The main purpose is to provide a model to improve the quality of the product by evaluating the performance of quality health in production lines able  to rank by providing appropriate quality indicators to identify, formulate and achieve corrective measures. Accompanied with accurate problem solving and operational scheduling according to the most efficient organization/production line and so investigating the source of the problem and preventing the occurrence of the problem. Because determining the direction of performance and key performance indicators (KPI) of the organization and measuring them to increase its health efficiency requires an efficient and integrated system. On the other hand, creating a homogeneous and orderly development process between the elements of the organization as a common language to solve the quality problems by aiming the improvement of the performance, customer satisfaction, sustainable production and cost management has been proposed.

Due to the nature of activities and processes, the petrochemical industry causes the production of industrial effluents, emissions and wastes that have adverse effects on the environment. The purpose of this study is to investigate the effect of petrochemical economic activities on environmental factors. In this paper, in order to minimize the costs and the amount of pollution caused by the emission of harmful gases, the closed-loop green supply chain model has been used, in which direct and reverse logistics networks have been considered. As a result, a fuzzy mathematical programming model has been developed for when the data are not definitively known. After the demand parameters and the amount of pollution are considered fuzzy, the maximum and bisector mean methods of the area are considered as methods (diffusion) of comparison and ranking of fuzzy definite numbers, and by adding Limitations of these two methods, the model was developed. To solve the model with real data, a plant from the petrochemical industry was selected and the data were prepared for a solution with very good estimates. Finally, the colonial competition algorithm was used to solve it. According to the model, its applicability was shown to reduce the number of environmental pollutants along with the reduction of transportation and waste, and the model for the closed-loop supply chain, which simultaneously considers two direct and inverse logistics networks. It is appropriate.

This study aimed to demonstrate the inequality of income and wealth in Iran. Therefore, the effects of income inequality and wealth distribution were modeled from 1390 to 1397 on Piketti's dynamic model and based on the analysis of household budget data for the first time in Iran. The results showed that Piketti's hypotheses are valid for Iran and fifty percent of total household wealth belongs to the tenth decile. Furthermore, high Income-share lead to more saving rate and widen the wealth gap between the top and low deciles. It is worth mentioning that this study results owning to the limitation of data gathered, mainly include real estate capital and omit capital like bonds, gold, foreign currency, stocks, and deposits. Consequently, the obtained results are conservative and including the above-mentioned omitted capital into the model would deteriorate the status of inequality. Undoubtedly, establishing a new capital gains tax system would improve equality.

Today, sustainable activities are essential for maintaining environmental sustainability, and have attracted a significant part of the environmental economy. The occurrence of environmental pollution in the industry and its dissemination throughout the supply chain tend to overcome sustainability. The purpose of this paper is to identify environmental indicators that affect the sustainable supply chain in the cement industry and rank the chain of the industry in such a way as to prevent the occurrence of Prevention.

For this purpose, factor analysis and fuzzy vikor decision making technique have been used.

Effective indicators are identified and explained in four levels of sustainability, strategic, process and operational. Then, according to the determined indicators of environmental performance of the sustainable supply chain, 42 factories listed on Tehran stock exchange ranked. The results of the implementation of the model show that the stability level has the highest correlation with the current variable supply chain performance and this correlation is 44%. Operating surface weight was 40%, the strategic level was 23% and the process level was 12%.

The results show that sustainability indices in the sustainable supply chain of the cement industry are more important in preventing the occurrence of environmental Prevention. And the sustainable supply chain of the Ilam factory has been ranked in the first rank and supply chain of Bagher Factory based on environmental indicators.

The bank is an economic entity that carries out the tasks of equipping and distributing credit, financial operations, currency trading, documentary and dividend claims, stock maintenance and securities. The National Bank of Iran has a staff of over 45,000 people. The National Bank has 5 branches operating in Iran, 2 operating branches and 2 subsidiaries abroad. Branches and Evaluate Branch Productivity during 96 and 97 Years Using Data Envelopment Analysis (DEA) Model. For this purpose, the effective factors are first evaluated and then the DEA model is used to evaluate the performance of the branches in order to measure their efficiency and inefficiency. We then rank them using the AP model and finally measure the productivity of a branch relative to 96 and 97 using the Malmquist Productivity Coefficient model. In this paper, using the data analysis approach, the performance of the West Bank Branches of Tehran National Bank in two years has been evaluated. The efficient and inefficient units of this branch are then identified by the CCR model and finally the efficient branches are ranked with the Anderson Peterson (AP) model and finally the Malmquist efficiency model is measured.

The main goal of the present study is to evaluate the total energy productivity and the effective factors on 134 manufacturing industries in Iran with a four digit code and also ISIC international classification among 1385 to 1394. To do this, with data envelopment analysis and Malmquist method the technical, technological efficiency and the total energy productivity are calculated on the manufacturing industries in Iran. And then by fixed effects method and generalized method of moments  (GMM),the effect factors on the total Energy Productivity was calculated. The results of the model showed that the total Manufacturing Industries experiences a technological positive growth, but some have been technically efficient. In all, the findings showed the growth of the total energy productivity in the manufacturing industries. The findings based on the model showed that variables such as skills, information communication technologies (ICT), research and development, physical capital and machinery and equipment , export and time are meaningful effect and just variable education is meaningless effect on the total energy productivity. Based on the results, reviewing the work style, improving managerial skills ,Government subsidized support of production and investment, imports tariff reduction of capital equipment and machinery of the industries with less energy consumption is recommended.

The purpose of this paper is to present an appropriate model of data envelopment analysis with a network structure for evaluating the sustainability of the supply chain of cement companies present in the Iranian Stock Exchange during 2014-2015 and determine their productivity based on the Malmquist index. This model attempts to introduce sustainability criteria in the supply network with regard to quantitative and qualitative constraints as well as undesirable outputs and Through their measure of supply chain, results are more consistent with reality. Overall, the results indicate a decline in the total productivity of these companies; meanwhile, only 7 companies have maintained their performance and other companies have had a performance fluctuation.

This paper presents a model for assessing the efficiency of e-commerce in the provinces of IRAN with data envelopment analysis (DEA). For this purpose, first, we assess the factors that affect to growth and use of e-commerce in Iran, then to show the efficiency of IRAN's provinces on e-commerce, data analysis model applied. Results show the average efficiency in IRAN in year 2016 compared to year 2013 increased. The numbers of efficiency in years 2014, 2015 and 2016 have a normal distribution, and only in 2013 we have outliers. Also MALMQUIST efficiency coefficient calculation results show we have progress in years 2015 and 2014 compared to 2014 and 2013.The most progress is in year 2014 that related to Isfahan province and in year 2015 related to Charmahalobakhtyari province. Ultimately the efficiency coefficient of MALMQUIST results 8 provinces in year 2016 have progress compared to year 2015 and 22 provinces during the same time period have regressed. The greatest progress in year 2016 is relating to Khuzestan provinces and most regression is relating to North Khorasan Province.

The purpose of this paper is to present a new model of data envelopment analysis with network structure to evaluate the performance of Tehran cement companies. For this purpose, cement companies listed on the stock exchange during the years 93-95 were evaluated. The results showed that the new model can evaluate the performance of these factories - which have a network structure and include multiple indicators - and, in addition to commonly used financial and technical indicators, to consider undesirable outcomes and sustainability criteria in the supply network. Meanwhile, the mindset of decision makers about the preferential importance of some indicators relative to each other is also considered in the form of weight constraints to bring the efficiency closer to the real value. According to the results, the factories are divided into two categories: efficient and inefficient. Based on this new model, only 7 cement supply companies have been able to achieve efficiency within the three surveyed periods of the 42 listed companies.

This paper studies the effects of psychological, individual, product strategy, and situational factors on the impulse purchase behavior of customers in all Refah stores located in Tehran. The paper uses Betty and Ferrell Model to set forward and to test 4 hypothesis. All four hypothesis were confirmed. The results indicated that the selected factors had impact on the impulse behavior of the consumers. Based on the results corresponding recommendations were finally made for the store management to improve their customer satisfaction, market share and competitive position.