جستجوی مقالات مرتبط با کلیدواژه « Optimization » در نشریات گروه « اقتصاد »

Banks face credit risk in the process of providing facilities based on the amount of resources provided. In the meantime, facility portfolio management can be effective in reducing credit risk by optimally allocating resources to economic sectors. In this research, the portfolio of Sina Bank's facilities is determined by using the Markowitz modern portfolio model and meta-heuristic algorithms of genetics and firefly. Comparing the performance of the models indicates the greatest efficiency of the genetic algorithm model in optimizing the bank's facilities portfolio. the results of the estimation of this model show that the service & commercial, housing & construction sectors have the largest share in the optimal portfolio of the bank's facilities. Industry & mining, agriculture & water sectors are considered risky assets of Sina Bank. the process of granting facilities has not been optimal. To reduce the credit risk of that bank's facilities, 52.4% should be allocated to the service & commercial sector, 40.7% to the housing & construction sector, 3.5% to the industry & mining sector, 3.4% to be allocated to agriculture & water sector.

Choosing the optimal stock portfolio is one of the main goals of capital management. Today, There are several tools and techniques for measuring portfolio risk and selecting the optimal stock portfolio. In this article, using data of 15 shares selected by purposeful sampling method from the top companies of Tehran Stock Exchange Organization including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days and then using MATLAB software models The Metaheuristic Optimization of the Genetic Algorithm under the MSV Risk Criterion and the Particle Swarm Algorithm under the CVaR risk Criterion are Compared. The results show that the genetic algorithm model under MSV risk criterion is more efficient and less risky, therefore the genetic algorithm model under MSV risk criterion is more efficient than the particle swarm algorithm model under CVaR risk criterion.

The purpose of this study is to investigate the multi-period optimization model of capacity expansion planning in the electricity industry considering different scenarios of carbon dioxide emissions. In this study, using interperiod dynamic linear programming model, the capacity development of power plants has been designed and modeled in GAMS software.
The objective function of the model has been to minimize the total discounted present value of investment cost, operation and maintenance cost, fuel cost and environmental cost in a 30-year horizon and with the constraints of supply and demand balance, power system security operation, the upper boundary of newly installed capacity, resource potential and grid stability. The proposed model was evaluated in different scenarios and sensitivity analysis.
The results of the study showed that in the basic scenario without environmental considerations (reduction of production costs), only the capacity of fossil power plants should be expanded. Meanwhile, in the basic scenario with environmental considerations, the capacity of combined cycle, solar, atomic and wind power plants have been expanded by reducing production costs and environmental costs. Considering the environmental considerations, the generation share of steam and gas power plants in the planning horizon has decreased from 27.7 and 22.7 percent to 9.5 and 4 percent compared to the base year, but the generation share of combined cycle, nuclear, solar and wind power plants have increased from 41.8, 2.3, 0.1 and 0.1 percent to 72.7, 6.5, 2 and 2.3 percent. The investment cost (capacity expansion) of the basic scenario with environmental considerations compared to the basic scenario without environmental considerations will reach 183 billion dollars in the planning horizon with an increase of 60%, and the basic scenario with environmental considerations significantly increases the development costs. Also, in the scenario of the continuation of sanctions, when the maximum annual capacity of renewable power plants decreases, the environmental goals will not be met, and there is a need to invest in expanding the production capacity through fossil power plants. In the sensitivity analysis of the model, the results indicate that there is a need to invest in expanding the capacity of renewable power plants in the three cases of reducing the investment cost, increasing the carbon price, and fuel price.

Aim and Introduction :

In the last decade, the design of the green supply chain network has become very important due to the increase in competition in global markets to achieve success, which requires the simultaneous commitment and cooperation of suppliers, manufacturers and distributors in the form of a network. One of the generalizable fields of the green supply chain network is the aquatic processing industry as one of the most important food industries that has paid less attention to optimize its supply chain in order to provide environmental benefits. In recent decades, the demand for food has increased more than the capacity to provide resources for it. Hence, the traditional food supply chain can no longer effectively manage demand. The increase in world population, globalization, global warming, drought and groundwater crisis in recent years have led to the loss of natural resources needed for animal husbandry and the reduction of marine reserves in Iran as well as in other Middle Eastern countries. The expansion of aquaculture farms has not only contributed to the development of sustainable food in the country, but has also been very effective in preserving endangered species. The aquaculture industry has grown significantly in recent decades. In this industry, green supply chain network design seems to be critical for aquatic processing.

This study is a descriptive research and applied one in terms of purpose. In this research, after examining the theoretical foundations and experimental background, the network design was proposed. The aquatic supply network was designed in three levels, for four farms, three factories, and two customers. The first level includes aquaculture farms. The second level consists of models of aquatic processing factories. The third level includes customer networks. Of course, other levels of the aquaculture supply chain, i.e. retailers, can also be considered in network design. Then the appropriate mathematical model was formulated and validated with a case study. LP-metric method was used to solve this model. The GAMS software was used in this study.

In this research, the network of green aquatic management has been designed to achieve the goals of minimizing costs and carbon dioxide emissions and maximizing the capacity of units at three levels for four farms, three factories, and two customers. In this research, after drawing the network of the problem and explaining the mathematical model, it was solved using the comprehensive criterion method with p equal to 1, 2, and 3.The value of the objective function was calculated with p equal to 0.09. By increasing the value of p, it became clear that there is no distance between the values of the objective functions and the ideal values. Then, the value of decision and binary variables was calculated. The first factory has the highest aquatic receiving capacity from the second, third and fourth farms.

In order to achieve the goals with an emphasis on reducing the carbon footprint, the first factory should process the third product, the second factory should process all three types of products, and the third factory should process the second product. In this research, certainty is considered for the parameters. If the surrounding environment is very dynamic, this turbulence can increase the uncertainty of model parameters. Therefore, it is suggested to solve the research model assuming uncertainty in the parameters in other researches. In this research, the same weight has been considered for three purposes.In this study, it is assumed that the transportation cost between nodes depends only on the distance. It is important to note that the cost of transportation between two points can be affected by various factors. Therefore, it is suggested to pay attention to these factors in other researches. Using the research model can minimize the total costs in the aquaculture network and maximize the capacity of using the components of the entire chain, bringing the environmental destruction to the lowest level. Therefore, it is suggested to use this model. One of the factors affecting the production and emission of carbon is the number of car trips. In other words, the lower the number of trips (carrying times), the lower the emissions. It is suggested managers to use a means of transportation with a higher capacity. Timely servicing of goods transport vehicles can also be effective in carbon emissions. It is recommended to periodically repair the car. In the chain under study, most of the cars were worn out and without fuel consumption reduction technology. It is suggested to replace the old vehicles carrying goods with modern and high-tech vehicles. It is recommended to use gas-fueled vehicles for transporting goods.

The performance of urban transportation modes is a critical issue for people's mobility. This research analyses various transportation modes in Isfahan city, focusing on social cost minimization and comfort maximization. This study aims to determine the optimal combination of urban transportation modes by considering the appropriate comfort level in Isfahan. The study's results indicate that different allocation models are suitable for different cost-comfort combinations cases. All of these combinations, called Pareto points, are optimal and one of these points can be selected according to the policy-maker opinion. The input data are based on data of origin-destination obtained from the Deputy of Transportation and Traffic of Isfahan Municipality between 189 traffic areas. This data is reduced to 44 integrated areas for the sake of simplicity. The model used in this research is a bi-objective linear programming model. In this research, GAMS software is used for modeling, ArcGIS software is used for data mining, and finally, Excel and SPSS have been applied to analyze the results.

Growing popularity of smart devices and 5G Internet, loT technology has also developed. The increase in the number of smart objects has led to an increase in data volumes and computational loads on a large scale. For this reason, cloud computing is used as a solution for this amount of data. However, given the importance of service quality, the cloud computing solution may not be responsive to latency-sensitive requests. Allocating resources in cloudy computing also reduces the cost of latency. Dynamic programming has been used due to the large number of requests and problem constraints. The proposed method reduces the cost of latency for loT requests. In this study, the proposed system modeling and algorithms were implemented for four cases. In these cases, two proposed methods have been the contribution of this research that these proposed algorithms have seen a significant reduction in the total latency cost. But as expected, the backward algorithm had a better response than the forward algorithm.

Banks are one of the most important economic pillars of countries that play a significant role in their growth and development. In Iran, due to the expansion of the financial and monetary system, banks have practically taken on the task of collecting and allocating economic resources. One of the important tasks of banks is the optimal management of assets and liabilities with the aim of maximizing returns while minimizing risk. The results of research in this regard show that the optimal allocation of resources leads to increased income and reduced bank risk, will be. In this study, we intend to optimize the real structure by optimizing the structure of assets and liabilities of commercial banks including Mellat, Saderat, Tejarat, Parsian, Pasargad, Eghtesad Novin, Sina, Karafarin, Dey and Middle East using the Mean-Variance model. Letchr('39')s compare the real and optimal structure of banks and then separate the banks into two categories of privatized state-owned banks and fully private banks, examine the effect of management on how to manage the resources and expenses of banks.The research findings show that the combination of bankschr('39') resources and expenditures on some assets and liabilities, including credit facilities granted to customers and banks, as well as current and investment deposits, is significantly different. It is calculated with the optimal combination. The research results also show that the combination of assets, liabilities and equity of private banks is more similar to the optimal composition than privatized state-owned banks, and these banks have performed better in managing their resources and expenditures.

The main purpose of this study is to optimize the production capacity of small and large industries in the path of economic development; Markov-switching chain approach. In this study, with the help of econometric techniques and using the Markov-switching chain approach, the production capacity of each small and large industry during the years (2001-2016) was estimated. In order to determine the optimal level of production capacity in small and large industries with two-digit code ISIC.Rev.2 and ISIC.Rev.3 It was used more. In this method, after collecting data and converting current prices to fixed, econometric techniques, especially the logarithmic transcendental cost function method are used. The results of data analysis in large industries showed that; The actual production level is 97645.6 million rials per year. And is acceptable in terms of analysis and indicates the optimal level of production and the minimization point as a function of the average total cost. The average value of real output of industrial enterprises in large industries at the production level is less than the optimal value. In other words, the utilization of production capacity in these industries has been 59%. Also, the results of analysis in small industries showed that; The actual production level is 614213.8 million rials per year, which indicates the minimizing point as a function of the average cost of all small industries. The average value of real output of each industrial enterprises in the production sector of small industries was 654213.8 million rials per year,

One of the main activities of banks is the allocation of resources, one of the main aspects of which is determining the optimal combination of credits to various economic sectors. Lack of attention to the allocation of financial resources available to the bank, can cause serious damage to the entire economic system. In this study with the purpose of determining the optimal portfolio of the bank, each of the different activities of the creditor was considered as an asset. Given that the fluctuations in the ratio of lending credits collection are the main source of expected bank earnings fluctuations, so the amount of lending credits of each economic activity, which need to be collected are considered as return standard of, and its variance is consider as a risk. Also, in order to determine the range of expected return on assets, the historical average on each asset is calculated. Considering the necessity of reviewing this issue, in this paper, a multi-objective non-linear programming model using the firefly algorithm has been used to determine the optimal portfolio of agricultural bank credits. The main features of this research are the consideration of three optimistic pessimistic and hybrid strategies under different economic conditions. The results showed that the optimal pattern obtained from the firefly algorithm differs from the current model of distribution of bank credits.The present findings can provide managers with a roadmap for choosing the optimal portfolio consistent with the bank's preferences according to different estimates of return and risk, thus leading to the proper management of loans

The problem of hydropower plant profit maximization includes simultaneous programming of optimal utilization of water resources and participation in the power market. The present research was performed on a chain of hydropower plants within the Karoon river basin in Khuzestan Province (Iran) (i.e. Karoon 3, Karoon 1, and Masjid Soleyman hydropower plants). In this research nonlinear programming and dynamic systems were applied to the monthly data for the 2005–2015 period to optimize total revenue. This study calculated optimal water reserve and release of water from the dam reservoir as well as profitability of the considered hydropower plants for a period of 24 months. The profit maximization was performed considering the revenue generated from energy markets, reserve, by-services, and frequency control, along with production costs including, maintenance costs, and start and stop and penalty costs. Results indicated that profitability of the studied power plants was more sensitive to the water inflow of dams rather than fluctuations in electricity price. The results show that by using optimization methods, more profit can be obtained than the current operation of power plants.

In monetary theories, the interest rate is the link between financial conditions and the rate of physical capital accumulation. In Iran, this rate is determined normatively. The question is - has this method of determining interest rates worked properly or not? The purpose is to investigate the optimality of policymaker behavior in determining bank interest rates by explaining a multi-objective linear optimization model. For this purpose, first the components of the model including target functions (income and money supply) and constraints (private consumption, government spending and inflation) are estimated by ARDL model, based on data from 1358-1397.  Then the optimization model for all years of the period 1396-1387 has been solved using parametric technique. Comparing the values ​​obtained from solving the model with the real values ​​resulting from the policies shows: 1- The bank interest rate set by the policy maker during the period is not optimal and in achieving the maximum policy goals within the capacity 2. In an optimal structure, there is a positive relationship between real bank interest rates greater than zero and income, but this has a limit value. Therefore, the results of McKinnon and Shaw's research on the effect of bank profits greater than zero on economic growth in the optimal structure of the economy in Iran are confirmed. 3. Under optimal conditions, the implementation of contractionary monetary policy to reduce inflation with a low threshold If the money supply falls below this level, it will have the opposite effect

At the core of any investment lies the return on investment. To gain a favorable return, an investor should take investment-related risks. The interaction between risk and return can lead to decisions on asset allocation. A key strategy in investment discussions is diversification in investment portfolio. Investment strategy is undetermined in different assets such as security, gold, currency and cryptocurrency. Despite the temporary recession and success of certain assets, it is hard to prioritize investment among assets (in terms of risk and return) to ensure that the investor makes the highest profit at the lowest risk. Thus, the present research used the Mean-CVaR model along with the Extreme value theory (EVT) based on Copula’s theory to estimate the correlations among time series. It used the dynamic conditional correlation (DCC) estimation method to measure the joint distribution of assets regardless of the normality assumption of data, collinearity, priorities and weights of investment in assets as optimal values.

Theoretical Framework:

Diversification of conventional investment portfolio which only involves cash and security with alternative assets such as goods, currency and estate helps to decrease the correlation of assets and increase its resistance to severe changes in stock market. It, thus, helps to improve the performance of investment portfolio (Fischer & Lind-Braucher, 2010). Moreover, due to the low correlation between conventional assets and cryptocurrency and its high-efficiency, cryptocurrency is a good instrument to be combined with diverse investment portfolios and can increase the Sharpe ratio (Chuen et al., 2017).One assumption of the distribution of financial return time series is the normality of data. However, in actuality, many financial return time series are not normal. When the normality assumption is violated, Value at Risk (VaR) is a proper measure.Risk exposure value is not an integrated risk measurement and due to the lack of sub-aggregation property, may be inefficient in optimizing investment portfolio. Thus, researches introduced Conditional Value at Risk (CVaR) as an integrated measure and an alternative for VaR.

As the return on financial assets is marked by a fat tail and is not marked by a normal distribution of data, to better predict the distribution of series, EVT was used. Moreover, Copula’s theory was adopted and the structure of correlations among series as time-varying was modelled via the dynamic conditional correlation estimation and the joint distribution of assets was analyzed regardless of data normality and collinearity. Then, the Mean-CVaR model was used to estimate the risk exposure value and set the investment priorities and weights among assets in Tehran Stock Exchange, Gold Coin, USD and Bitcoin as optimized values.In this research, for an optimal allocation of investment among four above assets in daily return, Tehran Stock Exchange index (TEPIX) was used. The daily return on investment in Gold Coin (the old version) was estimated in Rials. That of USD was estimated in Rials in Tehran free market. Finally, the daily return on investment in Bitcoin was estimated in Rials between October 2014 and April 2018.

A negative correlation was estimated via DCC-Copula between certain assets, which shows that when a particular asset gains higher return on investment (than the mean value), the other asset does not follow the same trend. Thus, investors are capable of diversifying their portfolio accordingly.Prediction of return on assets showed that the highest expected daily return on investment belonged, respectively, to Bitcoin, Gold Coin, Dollar and Tehran Stock Exchange. Moreover, as the optimized weights showed for zero CVaR, due to the low variance, the greatest investment weight was investment in Tehran Stock Exchange. The higher the investor’s risk-taking, the greater the investment weight of Gold Coin and Bitcoin.

Conclusions & Suggestions:

Considering the investor’s risk-taking level, if s/he tolerates the least risks, s/he is suggested to make the most investment in securities. An increase in the minimum expected return on investment is followed by an increase in the investment share of Gold Coin and Bitcoin. Thus highly risk-taking investors are suggested to invest in Bitcoin and low risk-taking counterparts are suggested to invest in Gold Con. Considering the conditional Sharp (C-Sharp) ratio optimal portfolio indicated a better performance of various portfolios than any other asset, and the best performance of the portfolio includes Gold Coin with more than 70% and Dollars and Bitcoins with an equal weight. Furthermore, according to the C-Sharp ratio in the optimal portfolio, the minimum weight of Gold Coin is 60% and the maximum share of Dollar and Bitcoin is 20%.

Orthodox microeconomics decision-making models are based on optimization but in the real world, most of decisions are institutionally and based on habits, rules and predetermined solutions. In this paper we have combined some concepts from cognitive-behavioral economics and new institutional economics to characterize institutional decision-making. Evidences from cognitive and behavioral economics show that the mental costs of direct optimization, even in simplest forms, are high and agents tend to make decisions institutionally to reduce this mental costs. Coase and Williamson argue that "non-zero transaction costs" can result “hierarchy” replaced instead of “market” and similarly we argue that "non-zero decision making costs" can result “institutional decision making” replaced instead of “direct optimization”. Religious behavior is a kind of institutional behavior (following religious rules and habits) and considering this view that institutional decision making and direct optimization both are important and both have distinctive roles in decision making process can influence microfoundations of Islamic economics.

Determining the optimal level of oil and gas production from the upstream projects of the country is one of the main challenges in the formulation of Master Development Plans (MDP) for petroleum projects and impacts the return on investment and profitability of contracting parties, especially the contractor. In Iran's oil and gas contracts, since the development of the MDP is the responsibility of the Ministry of Petroleum, the production profile of petroleum projects is determined by the National Iranian Oil Company Management and Consolidated Planning Department, based solely on technical geology and submitted to the contractor. The purpose of this research is to introduce and extend the scope of operation research planning models in order to determine the optimum level of crude oil and gas production based on technical, geological and contractual elements in Iranian oil and gas projects. For this purpose, we first review different common models for determining the optimum level of oil and gas production. After identifying their deficiencies, a comprehensive model based on operation research planning procedures is proposed to determine the optimal level of oil and gas production in petroleum projects. The proposed model in this study is a branch of nonlinear dynamic optimization models in which the maximization of the current net present value of each oil and gas project for the target production level is set as the objective function and the technical, technological and contractual factors are considered as constraints. The results of this research show that the use of operation research planning models in determining the level of crude oil and gas production provides reliable results, and facilitates the development of an MDP for the development of oil and gas fields of the country by the MCP department of the NIOCJEL .

The purpose of this paper is to compare the two types of Islamic finance instruments (Murabahah and Salam contracts) by influencing the contracting parties' utility. In other words, in this paper, the behavior of the economic agent in the form of the consumer of intermediate goods and the producer of intermediate goods in a two-period economy for the contracts of Murabahah and Salam is examined and optimized, and then using the genetic algorithm , The model has a numerical solution and it is determined: the total expected utility of the producer and consumer of the intermediary commodity in the financing of Murabahah method is more than the sum of the expected utility of the producer and consumer of the intermediary commodity in the financing by the inductor. In economic terms, Murabahah's contract is related to the Salam Pareto Optimal. In other words, given the degree of risk aversion, the utility of the intermediate goods producer and consumer in the Murabahah contract is more than the Salam. In this article, the necessary conditions for maximizing the utility of the parties to the contract are also extracted in the contracts of the Salam and Murabahah.