بهینه سازی

The purpose of the present study was to investigate the relationship between self-management strategies and innovative behaviors in teaching of teachers working in non-profit schools. The research method was descriptive-correlation. The statistical population of the study consisted of 177 teachers working in non-profit schools in Khoy city. 121 people were selected as a statistical sample by simple random sampling. The data collection tools were standard self-management questionnaires (Feround & Baltz, 1992) and innovative behaviors in teaching (Johnson, 2014). To measure the validity of the research tool, face and content validity were used, and to measure the reliability of the research tool, Cronbach's alpha coefficient was used. Descriptive and inferential statistics were used to analyze the data using SPSS statistical software. The research findings showed that there was a significant relationship between self-management strategies and all its dimensions with innovative behaviors. There is a positive and significant relationship between innovative behavior in teachers' teaching. The results of the study showed that the selective strategy component had the highest and the optimization component the lowest relationship with innovative behavior.

The innovative self-collection service in urban logistics is proposed as an alternative to door-to-door delivery, offering environmental and social benefits. This research aims to investigate the level of awareness among the population regarding the new self-collection service for order deliveries by preparing a questionnaire and conducting interviews with residents of Tehran. It also aims to identify the factors influencing customers' choice of this service and to examine the relationships between these factors using the fuzzy DANP approach. Ultimately, the research seeks to present a mathematical model for covering self-collection centers within an urban logistics network, aiming to minimize the distance traveled by consumers. The study's findings indicate that the awareness of the self-collection service among the population in Iran is very low. However, there is a significant increase in the population's interest in this service when the collection points are located nearby, demonstrating the high potential of this innovative initiative. Furthermore, data analysis reveals that the criteria of 1) proximity of self-collection locations to customers, 2) cost differences between door-to-door delivery and self-collection services, and 3) the quality of services provided at these centers play a crucial role in advancing this new initiative. Finally, the research recommends public awareness and education about the environmental and economic benefits of this new service, mapping consumer purchasing habits, identifying strategic points, providing financial incentives and discounts, and enhancing service quality to increase customer acceptance of self-collection services, which will lead to improved performance of the urban logistics network.

The main goal of this research is to assess the readiness of countries for the Fourth Industrial Revolution. The study aims to provide a model using artificial intelligence algorithms, such as clustering and classification, to predict the movement of countries towards Industry 4.0.

The research utilizes fast-density clustering (FDC) to categorize countries into distinct clusters. Subsequently, the kNN and SVM algorithms are employed for modeling and predicting countries' movements. Additionally, swarm intelligence algorithms are used to optimize the performance of the algorithms.

One of the achievements of this research is the development of a composite index for the classification of countries and predicting their movement toward Industry 4.0. The countries are categorized into three clusters based on their readiness for the Fourth Industrial Revolution, and the characteristics and trends of each cluster are elucidated. Another finding is the optimization of the kNN and SVM algorithms, demonstrating their superior performance compared to other algorithms.

Originality: 

The proposed composite index outperforms similar algorithms, indicating the effectiveness of the proposed method in predicting countries' movement toward Industry 4.0. The study presented a comprehensive analysis of the Iran cluster, including its overall ranking and rankings within specific sub-indices. The findings offer insights into the strategic movement patterns of Iran and provide recommendations for advancing its technological capabilities. This research not only contributes to enhancing the country's readiness in technology but also aims to elevate the power and standing of the Islamic Republic of Iran, particularly in critical sectors such as national defense and security.

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

In general, inventory optimization is one of the most important techniques in the production system, because the high cost of an empty warehouse and the cost of losing customers can cause serious damage to a system. The basic idea for inventory is to provide flexibility for a system and protect the system against events such as stock out. Inventory capacity for each product or part is defined by demand, delivery time and part price. By balancing the supply and demand rates, the optimal inventory capacity can be achieved. It is necessary to use practical and effective techniques and solutions to reduce breakdowns to optimally use existing equipment and resources and reduce large costs in terms of energy wastage and repairs and repurchase of equipment. In this context, spare parts are one of the most important links in performing optimal maintenance and repairs and quickly returning equipment to the production line. In good management of spare parts, the inventory system of the warehouse will lead to the reduction of maintenance and repair costs, manpower and the duration of equipment failure and will ultimately help to increase productivity. This study aims to optimize the inventory level of spare parts for military vehicles using a mixed probability distribution.                                                           

Design/methodology/approach: 

First, based on the literature review and selected basic articles, the research gaps have been identified, and accordingly, a mathematical model has been developed and solved to optimize the spare parts of military vehicles. Then, a meta-inventive method has been used to solve the problem. This is because the use of meta-heuristic methods to check and analyze the sensitivity of inventory optimization models can lead to better and more accurate results, and also the use of these methods is relatively new and helps in solving optimization problems. Also, the problem studied in this research is a non-linear integer programming model, which has been used for the problems of medium and large dimensions due to the complexity of the problem. The meta-heuristic method based on the genetic algorithm has been applied to save the total costs. Finally, to prove the effectiveness of the model, the proposed model has been implemented in a case study on the parts of military vehicles in the 177th Brigade of Torbet Heydarieh.

Findings indicated that the optimal system cost value and the economic order value were obtained during specific iterations of the model, i.e., the first, fourth, and tenth iterations. These points implied the effectiveness of integrating Poisson and Exponential distributions in the model and optimizing the system performance in different scenarios. Such results emphasize the consistency and robustness of the proposed inventory management strategy, especially when demand fluctuates and supply challenges. As the model is subjected to more iterations, differences in results are observed, indicating the potential for variability with increasing iterations. For example, if the model considers 100 different problems or scenarios, different results may appear, although a general consistency in system behaviour is noted. This indicates flexibility in the modelling approach, where even significant changes in parameters such as inventory costs or lead times are unlikely to drastically change the economic value or efficiency of the system.

Research limitations: 

This research was conducted on a case-by-case basis on the parts of military vehicles of the 177th brigade of Torbat Heydarieh city, so it should be possible to generalize it to other organizations, and because it was typically a cross-sectional study, conclusions about on the causality might seem difficult.

Practical implications: 

The main challenge in the supply chain is to control inventory levels by determining the size of orders for each department during each period to optimize the objective function, which has been investigated in various studies because inventory optimization is one of the important and practical techniques for optimizing the economic value of the order and realizing a stable situation in a production system. This is particularly important since the high cost of an empty warehouse and the cost of losing customers can cause serious damage to a system

Social implications:

 Due to the specific conditions of embargo and restrictions on access to international markets, accurate inventory management can serve as a key tool to maintain efficiency and sustainability in military operations. This research recommends that relevant organizations continuously analyze and optimize their inventory levels using mathematical models and optimization algorithms such as genetic algorithms to avoid additional costs and at the same time, to ensure the supply of parts in times of need.

Originality/value: 

Predicting exactly what and how many spare parts are needed for the necessary equipment in a business and when they are needed to be available in its warehouse is an important issue to consider. These parts are identified and managed to support the functions of critical equipment, and the lack of critical spare parts during planned or unplanned repairs will significantly influence the overall effectiveness of the equipment.

IntroductionToday, with the passing of the traditional economy and the intensification of competition in new dimensions, the customer has become the main pillar and axis of all the activities of organizations; In such a way, from a competitive point of view, the survival and continuity of life of organizations depends on identifying and attracting new customers and maintaining existing customers. With the widespread use of information and communication technology by banks and financial and credit institutions, numerous requests and needs have been formed by customers and the services of the banking system have diversified to a great extent. Hence, banks and financial and credit institutions need a precise definition of customer needs in order to gain a competitive advantage. Customer satisfaction depends on the provision of appropriate services by the bank. Attracting and retaining clients along with the quality of service can greatly improve the efficiency of banks and thus be directly effective in increasing the profitability of banks, therefore managing and formulating the general strategies of the bank in attracting and retaining customers with the knowledge of how to behave the system is possible. Due to the increasing speed of changes in information technology, bank managers will need to proactively change and focus more on customer preferences.Methodology The purpose of this research is to provide a practical model to retain current customers and attract new customers in a financial institution, which directly affects the profitability and survival of the organization. Therefore, the basics and effective components of advertising were used in attracting and retaining customers in financial institutions. For this purpose, the main indicators affecting the performance of the organization were extracted from the research literature. By reviewing the researches of the last 10 years, the indicators of the theoretical model were developed. This work was done by analyzing previous studies, naming data and classifying data extracted from interviews, observations and notes. The common codes along with important variables from the researcher's point of view were entered into the Delphi questionnaire. In the first stage, 28 variables were identified based on the opinions of experts and previous studies. Then, the 28 variables identified in the first stage were entered into the Delphi questionnaire with a range of 1 to 10, and the average answers were presented as the result of each variable. According to the researcher's opinion, 5 points were considered as the acceptance threshold of the variables, and based on this, the total number of variables was reduced from 28 variables to 17 variables. In the next step, the Delphi questionnaire was again distributed among the experts and the average answers were presented as the result of each variable. At this stage, according to the researcher's opinion, 4 points were considered as the acceptance threshold of the variables and the number of variables was reduced from 17 variables to 11 variables. Finally, after summarizing the scores and following the steps of the Delphi method, 11 variables were extracted as the final variables of the research.Results and Discussion In the next step, fuzzy cognitive mapping method was used to determine the effect of variables on each other. This was done with the help of Net Draw software. Using this software, the importance coefficients of each research variable were extracted. The obtained coefficients for the research variables were used as input for the simulation part. The simulation process was carried out in Vensim software, and based on that, the cause-and-effect diagram, flow diagram and suggested scenarios were proposed. The model of advertising effectiveness in customer retention was presented. Based on this and according to the proposed scenarios, it was found that the amount of customer attraction increased by approximately 40% during the studied period.Conclusion Finally, by increasing the staff training budget, using information technology and appropriate advertising, as well as increasing employee participation, we will see a significant increase in customer attraction, which will have a direct impact on increasing the profitability of the organization, so the results obtained from this research can be a basis for explaining attraction strategies. Customers in other financial institutions. The results of this research show the functionality of the concept of systems dynamics in financial institutions. It should be kept in mind that this issue is considered a powerful decision-making tool for managers, with the help of which they get a complete picture of the organization's processes in the form of a system. In addition, with the increase in attracting customers, the profitability of financial institutions will directly increase.

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.

With the increasing penetration of renewable energies in microgrids, the need to use energy storage systems in order to improve the reliability of microgrids has received more attention than in the past. Energy management of micro-grids is not possible without considering the correct and fundamental planning of these systems. Therefore, in this paper, a new algorithm is proposed for ESS planning in order to manage the microgrid in a reliable way. Considering the impact of different microgrid operation strategies on the planning of energy storage systems, microgrid performance has been investigated in reliability-oriented and cost-oriented strategies. Also, ESS planning is done in both weekly and daily planning horizons.  In order to correctly consider the uncertainties related to the multi-objective programming problem, various probabilistic models for the parameters in the network have been presented and they have been expressed as mixed integer linear programming (MILP). problems by implementing the proposed method on a modified IEEE 33-bus test system, the effectiveness of the proposed method for evaluating the reliability of microgrids is tested and the obtained results confirm its efficiency.

The use of shock absorbers in various industries in order to reduce the fluctuations of equipment and air from premature depreciation and increase the useful life of parts has a special feature. In this research, the design and construction of the hydraulic shock absorber and the analysis of the characteristics and components of the parts and the impact of the working fluid used in the shock absorber have been discussed. In common shock absorbers used in the industry, hydraulic oil with a constant viscosity (Newtonian fluid) is used, and in this condition, due to the constant viscosity of the fluid, the stopping acceleration of the shock impacting the shock absorber is initially high, and with the passage of time, the acceleration . The stop is reduced, and this issue causes motion shock and hitting the body, and may cause injury or death. In this research, researchers and decision makers have investigated the rheological properties of the working fluid used in shock absorbers by applying the science of rheology, which has been highly regarded over the years. In fact, by using fluids whose viscosity is not constant (non-Newtonian) and whose viscosity changes due to various reasons such as shear stress change, the intelligent function of the receiver is affected. The use of these fluids is such that when the body collides with the bumper, due to the impact and the high cutting rate, the viscosity of the fluid is reduced and causes the body to experience less acceleration and stop. decrease with The shear rate and consequently the increase in viscosity of the fluid, the acceleration of the stop is increased until it finally becomes an object. It should be noted that the construction of such shocks is of interest in the world, and this is the first experience of industrial use of this shock in the world, which can confirm the laboratory results of improving the damping conditions. These shock absorbers are used in various industries, such as automotive industry, aviation, strengthening of buildings against earthquakes, as well as the elevator industry.

The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.

Attaining an efficient portfolio involves solving an optimization problem. There are numerous techniques and tools available to solve this issue. In this study, 15 stocks from companies listed on the Tehran Stock Exchange, including symbols such as Khapars, Khazamiya, Vepasar, Foulad, Akhabar, Kegel, Femli, Tapiko, Sepaha, Fazer, Fakhas, Shohbaran, Shefan, Qamro and Qathabat, were selected using cluster sampling. First, the daily returns of these stocks were calculated over a 5-year period from 2015 to 2020 (1183 days). The risk of the optimal investment portfolio was then calculated using the Mean-Semi Variance and Conditional Value at Risk models. These two criteria were compared using a classic solution method. Subsequently, the output data obtained from these calculations were compared using MATLAB software, employing the Particle Swarm Optimization algorithm under the Mean-Semi Variance risk criterion and the Genetic Algorithm under the Conditional Value at Risk criterion.

The results of this study indicate that the meta-heuristic Particle Swarm Optimization method yields a higher portfolio return ratio compared to the Genetic Algorithm in the Mean-Semi Variance risk criterion.

Originality / Value: 

This research utilizes multi-objective genetic algorithms and Particle Swarm Optimization, which are intelligent and novel algorithms, to minimize the objective function value using Conditional Value at Risk and Mean-Semi Variance criteria. These algorithms optimize the return and risk ratios of the stocks in the investment portfolio with the highest possible accuracy. Additionally, the efficiency comparison of these models using MATLAB software contributes an innovative aspect to this study.

This paper has investigated the inventory and financial flows in supply chains. Its purpose is to provide a method to optimize these two flows for chain members, where Return on Capital (ROC) is defined as the dependent variable, and cash conversion cycle (CCC) equation components, which show financial and physical flows, are formulated as independent variables. The data of chain members from six selected industries, including auto & parts, pharmacy, food, petrochemical, metal, and mining, have been extracted. Two scenarios, 1) revision of independent variables without a change in the cash conversion cycle of the entire supply chain, and 2) reducing the days of independent variables along with reducing the cash conversion cycle, have been defined. The problem is simulated using Agent-Based Modeling and NetLego software. Results of the first scenario indicate that if Days Inventory Outstanding (DIO) is reduced in downstream and transferred to upstream of the chain, and Days Payment Outstanding (DPO) in the upstream is shortened, ROC is improved for the entire chain. Also, the results of the second scenario show that, in proportion to the reduction of the cash conversion cycle through productivity under collaboration of chain members, the performance improvement of ROC is remarkable.

There is a kind of supply chain services that the products are not considered as a commodity, and a combination of products and service or only service is exchanged in these chains. Despite the importance of these supply chains, the research literature is not very rich and the need for further strengthening in this area is quite evident. Hospital service supply chains are also the type of service supply chains that should be taken into consideration. The aim of the current research was to optimize the supply chain of hospital services. For this purpose, library studies were first conducted and the research gap was extracted. Then, a multi-objective mathematical planning model was designed based on research innovation and its assumptions, the first objective was to minimize the cost, the second objective was to minimize the unrealized demand, and the third objective was to maximize the commitment of suppliers. After the solution, the model was validated based on fast-moving and non-moving goods, and the results showed that the parameters of the model all lead to a worsening of the solution, causing an increase in cost and unfulfilled demand and a decrease in employee commitment. But the goal of employee commitment has changed more than other goals due to the increase in parameters, and sometimes this commitment is reduced by 70%. The parameter of demand for fast-moving consumer goods has the greatest effect on cost and unrealized demand.

The project of establishing FARAJA ballistic protection equipment laboratory started a decade ago. The structure of this laboratory was predicted based on the requirements of National courthouse institute 010106. Because of misunderstandings in the contract's implementation, this laboratory has not yet been put into operation despite spending a lot of money. The research project of evaluating, updating and upgrading the FARAJA Ballistics Laboratory was defined to check the current state of the laboratory, identify deficiencies and solve solutions.

With field visits, the condition of the laboratory was checked. By studying the existing standards, the requirements were adapted to the existing equipment, which will be the road map for establishing the laboratory under the title of qualitative and quantitative requirements assessment.

Based on the defects found, it was determined according to the needs and existing structures. The standards were reviewed to determine their requirements. The evaluation of these hardware requirements was done to follow up the solutions to solve it.

The hardware for the implementation infrastructure of the National courthouse institute 010106 standard has been specified. This clarification, qualitative and quantitative needs assessment and providing solutions to define executive projects to fix defects are

Organizations are working to reduce warehousing and transportation costs, expanding supply chain management. Transportation and inventory are the two main elements in the supply chain. The issue of routing-inventory is the consistent distribution of the product from one or more distribution points among the consumer group, which minimizes the average cost of distribution along the planning horizon without creating a shortage for the organization is the primary goal of this study. This study addresses this issue in perishable products with a fixed lifespan. A very critical area of vehicle routing is the inventory routing issue. The most important objectives of this research were to review the studies conducted in the field of routing-inventory, inventory management, routing of perishable goods, implementing the cost minimization goal in the proposed mathematical model, and developing appropriate approaches to solve the proposed model under the assumptions of this research.

Given the nature of the problem that comes from NP-hard types, It is impossible to arrive at a definitive answer for its large dimensions, so this study proposes a method based on the ant colony algorithm algorithm, which solves the inventory routing problem under new assumptions. In this study, this issue was addressed in perishable products with a fixed lifespan.

It was shown that the proposed algorithm is capable of solving problems in different dimensions, especially where it seems impossible to achieve global optimality. Experiments on different benchmarks all confirmed the optimality of the results over competing algorithms and guided us to achieve the research objective of minimizing costs.

In the city of Qom, there is no dual water supply network with a separate meter inside the citizens' homes. On the other hand, high quality water sources do not meet all the drinking and non-drinking needs of the consumption areas. Therefore, the water and sewage company should respond to the drinking and non-drinking needs of the citizens by optimally managing the distribution of various available water resources. In this paper, the problem has been investigated in the form of two optimization mathematical models.In the first model, the current water allocation to citizens has been modeled and optimally solved, in which water resources should be allocated to citizens at the lowest cost, provided that all the needs of citizens are covered. In the second model, it is suggested that by scheduling the allocation of water resources, citizens will have access to quality water for drinking at a certain pre-determined time, and other consumption needs of citizens will be met with low quality water during the rest hours of the day and night.In the proposed model, both the allocation of water resources for the drinking and non-drinking needs of the citizens under the condition of covering all the needs of the citizens and the allocation of water resources with the lowest cost for the needs of the citizens are included. Finally, the application of the proposed model is presented with numerical analysis.

This study aims to explore and identify the effective strategies for enhancing customer engagement through the use of artificial intelligence (AI) technologies in online marketplaces. A qualitative research approach was adopted, focusing on semi-structured interviews with managers and experts in the fields of information technology and online marketing. Participants were selected based on their direct experience with AI technologies for customer engagement. The data were analyzed using content analysis to identify key themes and patterns. Four main themes were identified: Personalization Strategies, Interactive Strategies, Optimization Strategies, and Loyalty Strategies. Each theme comprised various categories and concepts, highlighting the diverse ways AI technologies can be utilized to improve customer interaction. Personalization strategies emphasized customized product recommendations and behavior-based offers. Interactive strategies focused on enhancing social media engagement and virtual technologies. Optimization strategies dealt with streamlining the shopping journey and site performance. Loyalty strategies underlined the importance of data-driven loyalty programs and post-purchase personalized experiences. Thus, by leveraging AI for personalization, interaction, optimization, and loyalty, businesses can significantly enhance the online customer experience, thereby fostering deeper relationships and increased customer loyalty.

logistics management is one of the most used and effective methods in cost reduction at different levels of use in successful organizations. According to the experience and successful organizations that can provide their services, prepare and support one of the important components. Among these are conducting readiness and support planning studies and intelligentization. It is the use of artificial intelligence to support new services that can be considered in all parts of the chain. In military forces - management, readiness and support issues often play an important role, especially that logistics units are one of the categories, which must be in a more complex environment to their activities with greater speed and ability than the coma of future military-discipline forces. It is of any size and is more about dynamics and speed. logistics is developed day by day and can recognize the needs of its environment at a suitable speed and change itself in a way that matches these needs. The purpose of this research is to provide a model for the development of logistics in FARAJA with artificial intelligence, which in the first place identifies, explains and prioritizes the need for logistics in a scientific, codified and up-to-date manner.

This research is mixed in terms of practical purpose, in terms of survey method and in terms of strategy. The statistical population of the research in the qualitative phase was 12 people, and the statistical sample of the research in the quantitative phase was 32 people. First, in the research, factors on logistics were obtained by studying previous researches, menus of the Supreme leader and holding qualitative meetings with experts.

The findings of the research indicate the positive effect of using artificial intelligence in preparation and support, which can be more effective, the efficiency and compatibility of preparation and support will be more. Finally, according to the existing situation of Faraja logistics other suggestions should be made with written planning and the use of committed and specialized forces at the expert and management level as solutions to overcome the challenges.

Today, effective talent management is one of the most important challenges in the field of human resources of organizations. The present study aims to identify the dimensions and components of talent management in optimizing career planning in a military organization with a mixed approach. In the qualitative section, the statistical population consisted of experts and to be aware of their views, through purposeful sampling, 12 interviews were conducted. In the quantitative part, all senior, middle and operational managers are 186 people and the sample size is calculated using Cochran's formula and through stratified sampling, 125 people. Data were collected in the qualitative part by semi-structured interview method and in the quantitative part by a researcher-made questionnaire. Formal validity and content validity were examined and confirmed by summarizing the opinions of experts and the reliability of the questionnaire was confirmed by Cronbach's alpha test (more than 0.7). The results of the qualitative section show that talent management includes the components of identification, recruitment-retention, development and training, service compensation system, performance management, learning management, succession planning and career planning, including components of training, promotion and career turnover, Evaluation and review, Staff maintenance. The results of the quantitative section also show that in the field of talent management, the component of identification ,recruitment and employment is in the first place and in the field of career planning, the retention component is  the most important components.

In this paper we try to simulate the life cycle of the electricity industry using the agent-based simulation method. For this purpose, 5 Agents were mentioned and they were simulated by Anylogic software .Then, four scenarios were investigated with experts' opinions. In the first scenario, it is assumed that the attractiveness of gas power and combined has been reduced and the attractiveness of the other two power has been added, the result of this scenario is the increase in hydroelectric power production, which is not cost effective due to the lack of water resources for the country. In the second scenario, with the arrival of a new technology, the household consumer decreased by 40%, and hydro and steam power with a capacity of 40% were working in the power generation cycle, the result of this scenario is the reduction of fuel consumption and also the shortage of electricity produced relative to the electricity consumption. In the third scenario, it is assumed that the consumption rate of all consumer factors has decreased, which results in the lack of investor factor investment. the fourth scenario assumes that all consumers' consumption is constant, as well as keeping the attractiveness developed by the government steady. As a result, the capacity of electricity generation has increased, but we are still facing power shortages.