جستجوی مقالات مرتبط با کلیدواژه "شبیه سازی" در نشریات گروه "علوم انسانی"

In order to calculate the employment trend in Iran, the aim of the article is to optimize the employment trend in Iran for the years of implementation of the sixth economic, social and cultural development program (1396-1400). For this purpose, firstly, using the Gauss-Seidel algorithm, the system of dynamic and non-linear macrometric equations and the quantitative goals of the sixth development program, the employment process in the country was simulated. Then, the optimal trend of the working population was calculated from the minimization of the welfare loss function of the policymaker by programming and implementing an open-loop algorithm and solving the Bellman equations. The obtained results showed that achieving higher employment requires controlling inflation at lower rates as well as achieving a higher economic growth rate. The findings of this research are compatible with the theory of new Keynesians such as Gerish, Clarida, Gertler, Menkiu, Walsh and Taylor regarding the necessity of simultaneously reducing inflation and increasing production. Therefore, the jump in production and curbing inflation can be the optimal control of employment and business at the level Make the goal possible. Based on this, the targeting of macroeconomic indicators in the available (possible) territory, complete monetary and financial discipline, independence of the central bank and the use of stochastic optimal control theory in the targeting of development programs are among the policy recommendations of this article.

Taxation is one of the primary fiscal policy tools available to the government, playing a crucial role in economic growth, price stability, and promoting social equity. The value-added tax (VAT) law serves as an effective alternative to many indirect taxes and fees that cause significant distortions across economic sectors. VAT, by addressing shortcomings in the tax system, can provide a reliable income source for the government. Since the introduction of a pilot VAT law in 2008 and the enactment of a permanent law in 2021, Iran’s tax system has faced various legal and operational challenges. This study simulates the effects of VAT rate increase shocks across three scenarios (12%, 15%, and 20%) on Iran’s economic sectors using a computable general equilibrium (CGE) model based on the ORANI-G-Iran model and a 2016 input-output table. The results indicate that higher VAT rates lead to increased inflation and investment while reducing GDP and consumption.

This paper discusses the impacts of digital technologies on the effectiveness of chemical engineering education and their potential to enhance students’ professional skills. The integration of digital technologies, such as spreadsheets, mathematical softwares, industrial simulators, virtual and augmented reality, and artificial intelligence tools, has revolutionized the field of chemical engineering education by creating interactive and contemporary learning environments. These tools can enhance the comprehension and application of concepts and facilitate problem analysis and solving. They also provide virtual practical experiences and stimulate greater engagement with the subject matter among students. However, the adoption of digital technologies also presents certain challenges that should be addressed carefully. These include the need for adequate infrastructure, training, and appropriate assessment methods for fair evaluation of student performance. Despite these challenges, the effective utilization of digital technologies in chemical engineering education can enable students to cultivate and refine their technical and professional skills. Moreover, it can assist professors and researchers in developing advanced tools and improving the overall educational experience.

Ensuring the safety and health of human resources is one of the most important tasks that every organization must deal with in order to maintain its competent workforce. The results of the occupational health and safety management system lead to the prevention of work-related injuries and illnesses to workers and create a healthy and safe working environment. But the implementation of this system and its maintenance is not always successful so that the organization can benefit from it. The current research, using the system dynamics method, analyses the factors affecting the successful implementation of an ISO 45001 occupational health and safety management system in water and wastewater companies and it simulates a model of factors affecting ISO 45001. The statistical population of the current research is experts in the field of quality of water and waterwaste companies and universities. First, the factors affecting the implementation of ISO 45001 were extracted from the research literature, and then by interviewing experts, the necessary variables were identified and the causal-flow anf flow charts of factors were mapped using Vansim software. At the end, the system dynamics model of ISO 45001 factors system was simulated. The results of the model simulation showed that when implementing ISO 45001 and maintaining this system, the support of senior managers, the selection of a good consultant for the organization in order to reduce the complexity of procedures and instructions, and also pay attention to the skill level of internal auditors. plays a role. Also, the duration of the consultant's cooperation with the organization is effective in the successful implementation of this system.

The war game is an applied discipline aimed at serious games, while being professionally distinguished from futures studies, has its own special requirements and scientific community. Alongside the distinction, similarity of the two disciplines is significant, especially by non-educational games at the political and strategic levels. The problem is that if the commonalities and areas of synergy between the two disciplines are not well understood, the futile competition between the experts of the two disciplines will increase. For this purpose, in addition to archival studies and holding focus group meetings consisting of wargaming, futures studies and strategic studies activists, qualitative content analysis was used to professionally infer the relationship between the two fields.It is possible to see the convergence of two disciplines in theoretical foundations, having common goals and areas of practical synergy, as well as the possibility of synergy in the field of method. These two have practical synergy with each other in cultivating agile thinkers and decision makers, interdisciplinary and transdisciplinary teaming, seeking participation from divergent thoughts, problem-oriented environment analysis, knowledge sharing, and upskilling in methods and technics. In the future, it is necessary to examine more closely the relationship between the scientific and administrative processes of the two fields for the educational and research synergy between the scientific centers of war games and futures, as well as the development of common tools and systems, which is reflected in the development of the idea of “futures lab”.

The ideal development of the rural tourism industry requires an extensive knowledge of its key determinants and the associated issues. Against this background, this study was to present a model for simulating rural tourism development (RTD) in Deh-e Bala villages, Taft County, Yazd, Iran, given the major determinants. Utilizing a mixed-methods (qualitative-quantitative) approach, this applied study implemented a descriptive-analytical design. The statistical population in the qualitative part comprised university professors and tourism managers, but only university professors were recruited in the quantitative part. Snowball sampling was also practiced to reach theoretical saturation in the qualitative part, while purposive sampling was performed in the quantitative one. The data collection tools included interviews and questionnaires. In the qualitative part, thematic analysis was completed to identify the RTD determinants, which were then prioritized via the fuzzy Delphi method (FDM). At last, the RTD model was simulated in the quantitative part through agent-based modeling (ABM). The data analysis was done using the MAXQDA 20 software in the qualitative part and the Microsoft Excel and AnyLogic software packages in the quantitative one, contributing to the emergence of 44 primary codes, ten sub-themes, and four main themes (i.e., managerial, infrastructural, economic, and social factors). In this context, the managerial factor was labeled the most effective one. Moreover, the simulation output revealed that the RTD rate in Deh-e Bala villages, Taft County, Yazd, Iran, was over 95%. Along with the study results, tourism managers are suggested to dedicate much more attention to the key determinants and their leading roles in RTD.

The electricity sector plays a crucial role in the country's economy. Therefore, any disruptions in the supply chain of this industry can result in the loss of economic benefits and decrease the competitiveness of industries dependent on this sector. The industry structure analysis of the electricity sector has shown that the lack of proper relationships between entities involved in the supply of goods and equipment leads to disruptions in the electricity supply chain. On the other hand, the specific political and economic conditions of the country, the presence of natural disasters, and high levels of change in the Middle East region have had significant impacts on increasing uncertainty at various levels of the supply chain in this sector. Considering the high uncertainty in the procurement of components in this industry, this study focuses on presenting a set of scenarios for replenishing goods in the supply chain entities of this sector. To achieve this, a probabilistic four-echelon model consisting of a supplier, distributor, retailer, and customer was presented to minimize total inventory costs and the ratio of unmet customer demand based on the (R, Q) policy. Furthermore, by searching organizational documents, interviewing industry experts, and utilizing warehouse management software, data for the model was collected. Subsequently, through experimental design, initial solutions were provided for the differential evolutionary algorithm, and based on this algorithm, different values for reorder points and order quantities were determined. By employing simulation methods, the model's objective values were estimated, and the set of solutions was illustrated in a Pareto chart. Research findings have shown that increasing the average inventory levels of retailers' warehouses leads to a decrease in the proportion of unmet customer demand. This occurs when different reorder point values for two retailers have high levels, but considering the probabilistic demand function, the order quantity can vary. On the other hand, reducing ordering and inventory costs leads to an increase in unmet customer demand. In other words, when reorder point values and low order quantities lead to inventory reduction, customer dissatisfaction increases. The Differential Evolution Algorithm used in this study has accelerated the process of finding solutions and improved model efficiency. This algorithm considers values between high and low levels of reorder points and order quantities, presenting multiple objective function values. Utilizing simulation methods to estimate the probabilistic objective functions employed in the model has increased the speed of executing multiple scenarios, aiding in cost reduction and model execution time. Based on the results of this research, electric equipment with high innovation should have a low reorder point and a high order quantity in the supply chain. This is because the short product lifespan renders the product obsolete along the chain, lacking customer demand and consequently increasing the chain's costs. The computational results of this study indicate that a 105% increase in inventory leads to a 104% increase in customer satisfaction and a 95% decrease in lost sales costs. However, considering the high purchasing costs and large product volume during ordering, a scenario aligned with financial conditions and warehouse capacity should be selected for chain entities.

According to Article 16 of the Knowledge-Based Production Leap Law, all industries that consume more than 2 megawatts of electricity must use 5% of their electricity consumption from renewable energy within 5 years. The model is optimized from the transient simulation method using Transis software, using the test design method, with the help of the response surface method. Two independent factors, the area of the solar panels and the power of the diesel generator have been selected as the main variables, and then the total electricity consumption, the total gas consumption, the total diesel fuel consumption, as well as the return on investment have been selected as the economic answer for optimization. It shows that increasing the area of solar panels with a low slope increases the investment return period. On the other hand, increasing the power of the diesel generator to about 2000 kilowatts helps to reduce the investment return period. But increasing the power of the diesel generator from 2000 to 3000 kilowatts increases the period of return on investment. Optimization using the response surface method showed that the optimal point occurs in the panel area of 11716.89 square meters and in the diesel generator power of 1986.69 kilowatts, the return on investment is 1.612 years, the results showed that the maximum value of the utility function is 0.725. which is a number very close to the maximum possible value (that is, the ideal state whose utility function is equal to 1

The main objective of the present study was to design a simulation model for development of organizational citizenship behavior (OCB) in Gas Company of Yazd province. A mixed method (qualitative and quantitative) approach was carried out in this research which was applied in terms of purpose and descriptive-analytical in terms of nature and method. In the qualitative step, the statistical population included university faculty members, managers, assistants, and experts of Yazd Gas Company, and in the quantitative part, it included university faculty members. Moreover, in the qualitative part, snowball sampling was performed until reaching theoretical saturation, and in the quantitative part, it was done by judgmental sampling. The data collection tools were interviews and questionnaires. In the qualitative section, using thematic analysis method, the effective factors on OCBs were identified. Then, the identified effective factors were prioritized using the fuzzy Delphi method, and finally, in the quantitative section, the organizational citizenship behavior development model was simulated using agent-based modeling approach. The data analysis led to the identification of 50 initial codes, 16 sub-themes and 4 main themes as below: individual, managerial, organizational and environmental factors. According to our results, managerial factor was recognized as the most effective factor. The simulation results illustrate that the presented model can provide an appropriate assessment of the future development of OCB in Gas Company of Yazd province

The pan evaporation is used as a practical parameter in various fields, such as estimating water loss from lakes and dams, as well as estimating the Plant water requirement, especially in areas where there is no lysimeter information. Modeling this parameter can be useful in the estimation of missing data and long-term planning of water resources and agricultural development. In this research, by using an artificial intelligence model (Gene Expression Programming, GEP) and two time series models (Fourier and ARIMA), evaporation from the pan at Zayanderud dam station, was modeled in the period from 1344 to 1396 (53 years). The time series of pan evaporation on daily scale for the months of June (Khordad), July (Tir), August (Mordad), September (Shahrivar), and October (Mehr) as input to the Fourier and ARIMA models and 4 different patterns including the use of daily evaporation data 1 month, 2 months, 3 months and 4 months ago, were used as input for gene expression programming model. The results showed that the GEP model has acceptable results only in Mehr, and for other months the results are not acceptable in terms of statistical indicators. The daily evaporation estimated error was found to be 0.38 mm in Mehr. This error was acceptable based on the coefficient of determination of 0.84, the Nash-Sutcliffe coefficient of 0.83 and the Willmott's index of  agreement of 0.95. Unlike the gene expression programming model, the Fourier model provided acceptable results in all the studied months. The error values were obtained between 1.02 and 0.7 mm per day in all the studied months, which is equivalent to 5.2 to 8.8 percent. Comparing the results of the above two models with the ARIMA model showed that, the error values of the ARIMA model in all months are higher (9.4 to 19.6%) than the Fourier model, and Gene Expression Programming model. Therefore, the best model for estimating pan evaporation in October (Mehr) is the Gene Expression Programming model and in the rest of the months, the Fourier model. The evaluation of the model in estimating the extremes daily evaporation data also showed that the highest and lowest accuracy is in October (Mehr) and June (Khordad), respectively. In total, according to the statistical indices, the ability of the Fourier model to estimate the daily evaporation in the Zayanderud dam station was proved. Therefore, this model can be recommended to estimate the daily evaporation and also to reconstruct the missing data in this station

The importance of industry and its role in a country’s economic growth has been considered as one of the most important economic issues by researchers. In the meantime, the development of industry in Yazd Ostan and the over-establishment of industries in the Yazd-Ardakan plain have caused damage to water resources and increased pollution in this region. This study aims to investigate the resilience of water resources in the Yazd Ardakan plain in terms of industry establishment. In this research, industrial development has been analyzed, at different levels, using dynamic modeling in Vensim Software in the form of system dynamics analysis. The proposed model described industrial development, and using the simulation process, its behavior has been predicted in the next years. The model simulation results showed that the lack of development of large and water-intensive industrial projects in the region would cause the lowest amount of water resources loss in the Yazd-Ardakan Plain. They were determined as effective solutions to improve the resilience of water resources in the Yazd Ardakan plain.

The aim of this research is to examine the efficiency of stochastic differential equation models in predicting stock prices. To evaluate the accuracy of these models, a comparative study has been conducted between these models and conventional time series models. In this domain, Geometric Brownian Motion and Heston models have been reviewed. For this study, the shares of Mellat Bank with the ticker symbol ‘VBMELLAT’ listed on the Tehran Stock Exchange have been examined as a case study; for this purpose, the study has been conducted on the price data of these shares from the beginning of 2015 to the beginning of 2023. Before modeling the stock, price and making predictions, the possibility of recurring patterns and self-similarity in the price movement trend has been examined. The results indicate that Mellat Bank’s shares have long-term memory, which makes it somewhat possible to predict their behavior. Subsequently, the stock price prediction for the ‘VBMELLAT’ ticker symbol has been carried out, and the research findings show that the Heston stochastic differential model, based on most post-test evaluation criteria, performs better in predicting stock prices; such that this model only showed a 0.0464 percent absolute error in predictions. The AR time series model also performed acceptably with the key assumption that past patterns will repeat in the future. This assumption is consistent with the presence of long-term memory and stability in the overall index, which places the AR model second in the evaluation criteria after the Heston model. According to the results obtained, stochastic differential equation models are efficient models for modeling and predicting stock prices.

The development of innovative ecosystems leads to the overall enhancement of innovation and creativity in countries, subsequently contributing to their economic development. Since the presence of uncertainty and external disruptions affects the performance of the innovation ecosystem, the existence of resilient innovation ecosystems that can withstand disruptions and maintain their performance under any circumstances is of great importance in the current complex and changing environment. Therefore, this research calculates the resilience index of key players in the power industry innovation ecosystem through a case study, using three indicators: "lost performance level," " Robustness," and "recovery speed." The results show that, the innovation ecosystem had a moderate level of lost performance index in the face of the simulated disruption and is not in a favorable state in terms of recovery speed and Robustness. Based on this, policymakers and managers of this ecosystem need to consider measures to enhance its resilience and take effective decisions and strategic planning to improve its response to potential disruptions. Additionally, by utilizing the simulated results, they can observe the impact of implemented policies on the resilience of the studied ecosystem.

Temperature is a significant atmospheric element that manifests climate change, specifically global warming resulting from an increase in greenhouse gas concentration. Atmospheric simulation is a critical tool in studying changes in atmospheric-climatic elements, particularly temperature.
The most commonly used tool for simulating the responses of the climate to greenhouse gas increases and examining future temperature changes is the use of climate variables simulated by coupled atmosphere-ocean models (AOGCMs). General circulation models (GCMs) are powerful tools aimed at generating climate scenarios. However, GCMs cannot provide effective information on climate simulation at local and regional scales. Therefore, the downscaling method is used to bridge the gap between local and global scales.
The current research aims to simulate maximum temperature using an artificial neural network model that adopts data from the atmospheric general circulation model (HadCM3) under RCP8.5, RCP4.5, and RCP2.6 scenarios for the Yazd synoptic station from 2006 to 2095. The independent variable, as the input to the artificial neural network, was selected for statistical downscaling using four statistical criteria: Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. Finally, the maximum temperature of the Yazd synoptic station for the next century was simulated.

The present study aims to investigate the maximum temperature of Yazd's synoptic station in the context of climate change based on valid scenarios until 2095. To achieve this, three sets of data were used: average daily maximum temperature data from Yazd's synoptic station, observed atmospheric data for the period of 1961 to 2005 (NCEP data), and simulated data from 2006 to 2095 based on release RCP scenarios. The NCEP data from 1961 to 2005 included 26 atmospheric variables that will be used as independent or predictor variables.
Modeling, simulating, and forecasting temperature based on nonlinear and chaotic time series is a challenging task. Prior studies have shown that artificial neural networks (ANNs) are suitable for simulating and predicting basic processes that are not well known. It is crucial to select the correct input variables intelligently and according to the purpose of the artificial neural network's design for prediction and simulation. Accordingly, in this study, the most suitable atmospheric parameters as the input of the artificial neural network were selected by pre-processing and selecting the atmospheric variables for the base period (1961-2005) to simulate with four statistical criteria (Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry). The resulting mean square error (MSE) obtained from the statistical criteria was compared, and the correlation coefficient and the similarity of the monthly time series trend of the simulated values with the target values were also analyzed. The best network architecture was selected to simulate the maximum temperature of Yazd's synoptic station from 2006 to 2095 under different RCP emission scenarios.

The selection of explanatory variables for downscaling was based on four statistical methods Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. After analyzing the mean square error (MSE), correlation coefficient, monthly average values of the maximum temperature of Yazd station, and estimated values from 1961 to 2005, the probability density function, cumulative probability function, and monthly time series trend obtained from all four methods, the explanatory variables were selected. These variables include mean sea level pressure, the divergence of 1000 hPa, zonal wind component, zonal wind intensity of 850 and 500 hPa, altitude and vorticity of 500 hPa, average temperature, and relative humidity at a 2 m height.
The structure and architecture of the neural network were designed based on these selected variables. The network consisted of a two-layer feedforward, with a sigmoid transfer function in the hidden layer, a linear function in the output layer, an input layer with eight variables, eight neurons, and the Lunberg-Marquardt training algorithm. This architecture was used to simulate the maximum temperature of Yazd's synoptic station under RCP2.6, RCP4.5, and RCP8.5 scenarios for two periods of 2050-2006 and 2095-2051.
Comparing the monthly average values of RCPs (RCP2.6, RCP4.5, and RCP8.5) in the first statistical period (2050-2006) with the base period (1961-2005), the maximum temperature of Yazd station indicates an increase in temperature in winter, spring, and summer, and a decrease in the autumn season under all three RCPs.
Comparing the monthly mean values of RCPs (RCP2.6, RCP4.5, and RCP8.5) of the second period (2051-1995) with measured mean maximum temperature (2005-1961) showed that temperature will increase the most in winter, spring, and summer, similar to the first period of the RCP8.5 scenario. In this scenario, unlike the other scenarios, the increase in temperature is evident in both subperiods for the autumn season. Finally, in the second period (2051-1995), the increase in the average maximum temperature of Yazd station in winter, spring, and summer, and the decrease in the average maximum temperature in autumn will be more significant.

The increase in greenhouse gas concentration resulting from human industrial activities is expected to cause global and regional warming in the future. The current study's findings indicate that the average maximum temperature of Yazd station will rise between 0.4 to 6.9  in winter, 0.2 to 8.1  in spring, and 1.1 to 7.7 in summer from 2006 to 2095. However, a decrease in the maximum temperature between 0.6 and 1.4  is expected in autumn. These results are consistent with those of other researchers.

Applying a favorable tax system has important conditions such as justice and efficiency, therefore, consumption tax and income tax will comply with the principles of benefit and ability to pay. In this regard, value added tax is known as the most important innovation of the 20th century in terms of tax collection on consumption. Since increasing government revenue is one of the important goals of imposing this type of tax, the government has tried to determine the rate of this type of tax effectively and efficiently. Disproportionate increase in value added tax rates can have negative social effects on inflation, economic growth, income distribution, and general well-being in society. It may also have disruptive effects on other variables and sectors of Iran's economy. To manage the rate increase, one approach is to simulate and examine its consequences and effects on macroeconomic variables in the form of a multi-regional calculable general equilibrium model (MRCGE). Three different scenarios were applied and examined to simulate the shock effects of the increase in the value-added tax rate (12% , 15% , and 20 %) on four macro variables of Iran's economy: inflation, gross domestic product, consumption, and investment.  The simulations were conducted at the country level using a multi-regional calculable general balance model, known as the ORANI-G Iran model, using the 2016 input-output table and regional accounts of the country. The results indicate that the effect of increasing the tax rate on value-added will increase inflation and investment and decrease GDP and consumption.

Various techniques and approaches have been presented to solve multi-objective decision making problems under different assumptions. The STEM technique is also one of the most widely used techniques for dealing with this group of problems, especially for solving multi-objective linear programming (MOLP) models. In the current research, a new approach has been proposed as the development of the mentioned method. In this regard, the second phase of the method is integrated with the simulation process and the concept of utility function has been used in order to determine the probability of selecting targets. For each of the selected targets, several random adjustment rates are defined. Random selection of satisfactory functions based on their utility and using simulation tools, will "create diversity in the selected objectives for adjustment". Also, by considering different and random adjustment rates for selected functions, while facilitating the decision-making process and providing an analytical report to the decision-maker, it will be possible to apply "different levels of satisfaction" of the decision-maker. A three-objective problem with five constraints, is solved using the proposed method and its results are presented. The results indicate that applying the above changes will lead to solving some of the basic limitations of this method that have been mentioned in previous studies. Comparing the proposed technique with the basic method, shows the overall superiority of the proposed method, especially in the criteria related to interaction with the decision maker.

There is good reason to expect crude oil prices to follow nonlinear models. However, previous research has considered the linear assumption to investigate the existence of a unit root. Unit root linear tests such as ADF, PP, and KPSS are provided for linear models. These tests are not suitable for nonlinear time series. Because the model deviation from the linear state may be considered as a random permanent deviation. The purpose of this article is to test the nonlinear unit root of crude oil prices, specifically Brent and WTI oil in the period 2019-2020 daily. For several decades now, various classes of nonlinear models have been introduced. These models introduce a wider range of dynamics than linear models in time series. A special type of these models that economists pay attention to are TAR models. In these models, as in linear models, valid statistical analysis requires distinguishing between the deterministic trend and the stochastic trend. In this study, the Bayesian unit root test for the general SETAR (1) model has been used with respect to the necessary and sufficient conditions for the maintenance of SETAR processes based on the article by Petrocyl and Wolford (1984). A nonlinear unit root test was performed using Bayesian validity interval. The results show that Brent crude oil prices in both regimes contain a unit root that is consistent with similar findings for the production or consumption of crude oil..

There is a direct relationship between the number of machinery in any industrial problem, their costs of purchase,  and the space they occupy. Accordingly, due considerations about the constraints are required in space and budget. Workshop production problems under uncertainty conditions including more than two pieces of machinery are regarded as NP-hard. Therefore, using common research in operation techniques, one cannot embark upon modelling and finding the optimal answer for such problems. Hence, this study aims to propose a conceptual mathematical model for the problem of industrial production. The research problem involves cost limitations while working out the solution through simulation studies.

Design/methodology/approach: 

First a mathematical model has been proposed for workshop production with cost and space constraints. Then by enlisting the help of Arena software, 14 simulation models have been designed for machine ‘m’ and work ‘n’ of different production sequences. Finally, by solving a numerical model example, the problem of nearing the optimal condition of the problem, considering minimizing the objective function of the tardiness and earliness costs, has been resolved. This measure has been adopted to determine the optimal number of machinery, considering work prioritization laws in work cells by executing the model and scatter search algorithm and designing relevant experiments.

Solving a numerical example considering the default number of machinery and the LPT optimization method resulted in the best answer when the total tardiness and earliness costs were intended. In contrast, in the case of increasing the number of machineries, both the LPT (longest process time) and CR (critical ratio) methods led to the best answers with the help of dispersed search algorithms in simulation studies. The results included the cost and space constraints.

Research implications:

 One of the prominent features of simulation is that with the minimum distance between the problem and the real world (not removing the limitations and complexities for solving the problem, which is usually done by removing the facts in other problem-solving methods, to reduce the complexity and the possibility of solving the problem) problem-solving and decision-making scenarios are applied. This helps managers in decision-making. Most of the time, the physical study of systems is very difficult and costly, or even impossible; therefore, an alternative model should be used to study the system.

Originality/value: 

A careful investigation of previous research and relevant literature reveals that none of the studies in the literature have simultaneously dealt with the problem of the number of machinery in conditions of cost and space limitations. Therefore, the present study incorporated the mentioned constraints in the scheduling problems and adopted simulation problem-solving methods. It resulted in delving and enriching the subject further.

The main purpose of this study was to mapping the canopy cover (CC) and the aboveground net primary production (ANPP) of plant life forms by the independent factor of physiographic in the rangelands of Ardabil province.Material &

First, according to plant types and environmental factors, sampling was taken at the peak stage of plant growth from 2016 to 2020 using one square meter plots. To determine the most effective environmental factor in the changes of plant parameters, the correlation between dependent and independent variables was used. Linear regression equation was used for modeling by the most effective factor and the maps were simulated. The accuracy of the equations was also calculated by RMSE. Then, the correlation between CC and ANPP of life forms and the total was obtained.

The results showed that the elevation factor had the highest correlation with the dependent variables. The difference between the estimated average of the model with the measured values for CC of grasses equal to +1%, forbs –1%, shrubs -1%, and total CC +2%; and these values for the ANPP of grasses equal to +21 kg/ha, forbs was equal to zero, shrubs were -13 and the total ANPP was +274 kg/ha.

Predicting the studied parameters will be necessary to manage the balance between supply and demand of rangeland products.

The most important and perhaps the main concern of human rights has been formed with the emergence of new capabilities in the scientific and technological fields of biology. This has caused opposition to human simulation in jurisprudence and law. The purpose of this article is to examine the reasons for banning human cloning from the point of view of jurisprudence and criminal law. The current article is a descriptive analysis and has investigated the mentioned topic using the library method. The findings indicate that interference in God's creation, disintegration of the family system, ambiguity in kinship relations and disruption of the kinship status of cloned people are the most important jurisprudential reasons for banning human cloning. The guarantee of individual rights and freedoms of citizens based on bioethics is shown in the framework of principles that are called “bioethics principles”. These are the principles and goals that criminal law pursues in bioethics. The principle of harm, or in other words, the principle of harm, is one of the prominent principles of bioethics, which has received the most attention and discussion in the field of criminal law, and is one of the fundamental foundations of criminal law intervention. The other face of this principle appears in the field of criminal law in the form of “obligation to do no harm” and emphasizes the absence of harm.