فهرست مطالب amir afsar

Given the growing importance of project management and control issues and the benefits of choosing the right management method according to the specific type of project, The purpose of this study is to provide an appropriate method for planning and managing research and development projects. Uncertainty of activities and time required to complete each project activity, requires the project manager to use techniques appropriate to the specific nature of the project under review. In this paper, by describing the probabilistic project of constructing a model airplane according to the probabilistic conditions, a suitable algorithm based on Gert method and Monte Carlo simulation is presented in such a way that as a result the project manager can analyze the basic components from the output of the algorithm and estimate random variables such as the expected time of project completion and the average cost of completing project activities and extract the desired results to better manage and control the project. In order to provide a better understanding of the proposed algorithm, the results of using the algorithm to plan and manage the model aircraft construction project will be reviewed and explained and As a result, the expected time and cost of the project is extracted within a 95% confidence interval.

In general, numerous studies have paid a special attention to machine planning,job allocating andjob sequencing in scheduling problems to optimize makespan. Due to the relation among economy, energy and environmental concerns, energy use is one of the most important issues in different systems planning. In this paper, a scheduling of heterogeneous parallel machines is studied, in which the job process speed on every machine is settable. Since there is a direct link between used energy of machines and process speed, the purpose of the paper is to minimize total used energy and tardiness-related costs in delivering customers' demand. In order to optimizing the problem, two meta-heuristic algorithms, Memetic algorithm and Genetic algorithm, are developed, finally the results of both algorithms are analyzed and then compared to each other as well as to the results of the GAMS optimization software. Keyword: heterogeneous parallel machines scheduling, total lateness costs, Energy costs. Memetic algorithm

In the classic models of scheduling problems, researchers mostly concentrate on the objectives considering jobs completion time. Due to the relation among economy, energy and environmental concerns, attention to the energy use of machines have been considered by researchers in the field of scheduling in recent years. Also, In the literature of scheduling problems, it is mostly assumed that one agent try to optimize the problem. But, occasionally there are several agents that each has their own jobs and they must use a series of common resources to process them. In this study, a two-agent heterogeneous parallel-machines scheduling problem is studied in which the process speed of each job on each machine is adjustable. Since there is a direct link between the energy used in machines and process speed, the used energy costs affect on scheduling problem. In this study, the first agent is tried to minimize total tardiness penalty as well as energy costs of production machines and the second agent is tried to minimize total tardiness and earliness. The suitable schedule should be considered to allocate and sequence jobs of agents to the common resources to optimize appropriately the agent’s objective functions. Since the proposed problem is Np-hard, in order to solve it in large scale problems, a Memetic algorithm is developed and to verify the performance of this algorithm, we take into comparison the results of Memetic algorithm with the results of GAMS software and of another meta-heuristic algorithm.

Due to the entry of companies into the competition among the networks the ultimate success of companies depends on the ability of management to integrate the interconnected network of interfirm relationships. Hence, this has required the identification of the structural characteristics of the networks for their better management. Traditional modeling and analysis approaches focus on individual firms or use a dyadic lens. This approach, however, fails to account for the systemic effects resulting from the complex topological and behavioral aspects inherent in supply networks (SNs). In spite of economic importance, little is known about diversification in the structural shape or topology of SNs. Therefore, many researchers have emphasized the use of social network analysis as a tool for morphological analysis of SNs. In this regard, this study examines the structural characteristics of the supply network in the Iran automotive industry for understanding the structural difference between the high-performing companies and the others in their network. The results showed that high-performing companies are not properly configured to respond to market requirnments and have little agility in this regard. Also, they exhibit significant relational power, more timely access to resources, greater levels of knowledge sharing and learning among SN members. In other words, they manage their relationships in a way that facilitates sharing of knowledge and learning.

The emergence of ERP systems as an integrated and process-oriented information system that supporting all activities of the organization, has led to significant improvements in the performance of deploying organizations. One of the success factors in the implementation of ERP systems is the proper identification of risks and appropriate responses to them throughout the lifecycle. Therefore, this research with an innovative point of view seeks to design a risk control model for ERP implementation by the Grounded Theory (GT) approach during open, axial and selective coding steps. The present research is both fundamental and applied in terms of purpose. The statistical population of this study is ERP elites, interviews with 12 of these elites were conducted using purposeful sampling method. The results of the study showed 58 primary codes and 17 main categories that presented in the form of a paradigmatic model including; causal conditions (pre-implementation risks, implementation risks and post-implementation risks), phenomena (ERP implementation risk control), context (organizational factors, technical factors and human factors), intervening conditions (factors associated with employees, factors associated with project implementation and factors associated with the ERP system), strategies (path improvement, human resource development, organizational change, and Organizing) and consequences (organizational consequences, financial consequences and social consequences) and relationship between its different dimensions is also presented.

Human resource management today is more than ever encountered with the diversity of human resources in the organization. One of the factors of this diversity, in addition to other differences, is the difference between generations. The difference between generations has always been a problem. The purpose of the present research is to identify the generational differences between employees of public organizations. This research in terms of approach and strategy is in the category of qualitative research. Also, the present study is descriptive-exploratory type. The statistical population of the present study was executives and assistants of public agencies and organizations of Qazvin province. In order to identify the differences a semi-structured deep interview was conducted with 19 managers and deputies of executive and governmental agencies. After collecting data, theme analysis was used to analyze the interviews. The results showed that the staff of the studied organizations can be classified into three young, middle-aged and experienced generations. The extracted human resource differences related to the field of human resources were categorized into five dimensions :occupational, personality, educational, technical and motivational.

Recreational tourism is an important form of domestic tourism in Tehran, based on the statistics of the National Center of Statistics and the views of the experts. This paper tried to propose models for predicting effective variables on predicting domestic demands for recreational tourism in Tehran. The study used the monthly information between 2001 and 2015. The independent variable was the number of domestic recreational tourists in Tehran, and the dependent variables were selected based on Delphi and Fuzzy DEMATEL techniques. The model framework was a combination of regression, the fuzzy neural network, and SVR algorithm. The combinations of these methods helped measure prediction errors and compare methods. Results showed that the proposed hybrid approach of regression and Adaptive Neuro-Fuzzy Inference System (ANFIS) could have a better prediction compared to other methods for predicting domestic recreational tourism.

  One of the most important events in the tourism industry of each country is the demand for a product or destination and its true prediction of tourism. It should be noted that there are distances and deviations between actual values and predictions. The use of modern scientific and forecasting methods will make the results far more than an objective estimate and closer to the truth; this article pursues the same goal in the field of medical tourism. In the first step, factors affecting the demand for domestic medical tourism in Tehran were identified by 31 experts using Fuzzy Delphi and Dematel Fuzzy methods. The factors were then processed by MATLAB2017a software. After determining the demand function, and collecting monthly data of each effective factor from 2001 to 2015, three regression prediction models, a fuzzy neural network, and SVR algorithm were implemented using MATLAB software to measure and compare forecast errors. The demand function for domestic medical tourism included: economic factors (individual income and wealth), service prices and cost of living in the destination, the cost of accommodation facilities, air pollution, and the price of alternative products (foreign travel), the number of medical centers, hospitals and laboratories. The proposed hybrid approach for regression and SVR algorithm can make a better prediction compared with the other methods of forecasting domestic medical tourism. Therefore, it is recommended to use the demand function and forecasting model to lower the forecast error while planning for domestic medical tourism demand in Tehran.

Considering the broad approach of innovation and the with respect to the innovation development depending on social, economic, institutional of a region as well as path dependency, one of the basic principles of regional innovation policy is the lack of compatibility of policies of successful regions in other regions due to different policy context and path dependence. In developing countries such as Iran, due to the wide variety of cultural and territorial conditions, the importance of this issue is increasing. On the other hand, considering the increasing attention of policy makers to the issues of regional innovation development, such as the development of strategic plan for the development of provincial science and technology, correct understanding of this concept, as well as a conceptual framework is needed to allow for appropriate policy analysis and suggestions. In this research, in addition to explaining the cognitive map of knowledge in this area, the conceptual framework (IDCR) as well as its components, is described.

Most management decisions at all levels of the organization directly or indirectly depend on the state of future prediction, and through the same prediction that one can imagine a success or a status in the future, thereby minimizing risk or making any adjustments and adaptations in the program (Inskeep, 2004). One of the most important events in the tourism industry of each country is the demand for a product or destination of tourism. But it should be noted that predictions can never match 100% of what happens in practice (Claveria & Torra,2014). There will always be distances and deviations between actual and predicted values, but the use of scientific and modern methods of forecasting will cause the results to reach far more than an objective estimate to the truth (Shen & Song,2011). In recent years, with the changing pattern of holidays and the formation of short-term holidays, cities have found the opportunity for tourism development. One of the most important types of domestic tourism in Tehran, based on the statistics of the National Center of Statistics and the views of the experts in this area, is VFR tourism. A definition was proposed by Backer (2003: 4); ‘VFR Tourism is a form of tourism involving a visit whereby either (or both) the purpose of the trip or the type of accommodation involves visiting friends and/ or relatives.’ This definition encompasses all VFR typologies and was put forward as a model (Backer, 2008b) to aid in explaining VFR typologies.
Source: Backer, 2008b
As represented in the VFR Travel Definitional Model (table 1), VFRs can fall into a number of distinct groups. The category in the top left hand box depicts what could be considered to be the “pure” VFR, whose purpose is to visit their friends and or relatives and also stays with them. The lower left hand box depicts a less pure form of VFR traveler, who is staying with friends and relatives, but whose purpose of visit may be unrelated to VFR. This issue that VFR travelers can have a non-VFR purpose of visit when staying with friends or relatives has been discussed in previous research (Backer, 2003; Seaton & Palmer, 1997). Discussion and Results: For this purpose, the present study seeks to propose models for forecasting effective variables on forecasting domestic business tourism demand in Tehran. To do this, information was used between the years 2001 to 2015. Independent variable of this study is the number of domestic VFR tourists in Tehran, and dependent variables were selected based on Delphi and Fuzzy DEMATEL techniques. The model framework is a combination of regression, fuzzy neural network, and SVR algorithm, which combines these methods to measure forecast errors and compare the methods. The results of this research show that the proposed hybrid approach of regression and Adaptive Neuro-Fuzzy Inference System (ANFIS) can have better prediction than other methods for forecasting domestic VFR tourism.

Rapid changes in today's knowledge-based economy, has caused the organizations to achieve its purposes and sustain proven survival for the successful implementation of knowledge management. But if the organizational culture for the successful implementation of knowledge management is not provided, the desired outcome will be missed. The purpose of this study is to identify and prioritize the critical success cultural factors of Knowledge Management in E-Banking.
The method of this research is descriptive-analytical and Mellat bank of Qom province was selected as the statistical subjects. To prioritize critical factors of Fuzzy Analytic Hierarchy Process is used.
Findings: The results indicate that among the three main criteria, "knowledge-Based culture, corporate culture, knowledge creation and sharing culture" criteria are the most important.
Cultural factors play a crucial role in determining the results of knowledge management efforts. Therefore, organizations that intend to implement knowledge management need to study cultural factors affecting knowledge management.

Investing in the stock market, is a significant part of the country's economy. Increasing profits and reducing the risk of investing in the stock exchange has always been a major concern for investors. Also, Stock markets are affected not only by macroeconomic parameters but also by thousands of other factors. This research aims to provide a model in which future stock potential is forecasted by considering the technical analysis indicators by the fuzzy neural network. According to the forecasts, the mathematical model based on factors such as the return, variance, and skewness of the stock portfolio will be optimized. Then, this model is solved using the genetic algorithm. This research is an applied research in terms of purpose and is descriptive in terms of method. The empirical results shows that the proposed models will provide more profit to investors regarding variance and skewness comparing to traditional models and stock market index.

Supplier selection and quota allocation is an important decision in supply chains. This decision can be considered as a complex multi-criteria group decision making problem. This decision in many practical situations is very difficult for vague and uncertain environment. This vagueness and uncertaintycan be handled by using fuzzy set theory. Therefore, this paper proposed a fuzzy MCDM model to evaluate candidate suppliers and quota allocation. A hybrid ANP-VIKOR method in fuzzy environment applied first with 16 criteria to evaluate suppliers. Then, a fuzzy multi-objective mathematical model is used to quota allocation. Finally, the fuzzy model is solved by Tiwarimethod. An illustration with a data set from a realistic situation is presented to demonstrate the effectiveness of the proposed model.

Customer value refers to the potential interaction of customer and enterprise in the certain periods of time. As companies recognize customer value it can provide customized services for different customers, they can achieve to an effective customer relationship management. This research focuses on Banking Industry and integrates data mining techniques and management issues in order to systematically analyze the customer values. First it applies Fuzzy Analytical Hierarchy Processing (FAHP) in order to weighting variables and then imports DFMT model to the k-means technique, for clustering customers according to the specific criteria. Using proposed scoring model establishes the customer value pyramid and categorizes customers in four spectrums. The customer value pyramid helps to separately determining of each customer value to giving appropriate services to them in proportion with the class value. The statistical population was 285 customers of Tejarat bank branches of Zanjan city in Iran. In the resulted customer pyramid, the first spectrum is the Platinum customer which is composed of two rows of the pyramid called H1 and H2. These two rows in pyramid have the highest value and have the most profitability for the bank. Second spectrum, is called golden customers which has three rows in pyramid called H3, H4, H5. Third spectrums are Silver customers which are laid in H6, H7, H8 rows of spectrum. Forth spectrum, are leaden customers that are H9 and H10 rows of customer pyramid. This spectrum receives and wastes resources of the bank and bank should respect and bear high risks.

Credit institutions to provide variety of facilities to their customers, need to comprehensive studies by qualitative and quantitative aspects of their applicants. By this way, accomplish a complete evaluation of repay ability measure and calculate the refund facilities probability and finance services by them, these reviews generally validation name. The purpose of this study was ranking customer groups and specifies the best part of them until brokerage firm do its credit allocation process mechanically. Here, after the preprocessing of the data, they are processes in the RFM model. Then SOM neural network as one of the clustering algorithms will change customers to 10 cluster. Using the proposed model, the clusters will rank. The top clusters, identification and facilities grant operations to the members of these clusters will do. Finally, three clusters 5, 1 and 7 defines as top clusters that they are the target customers. Coefficient facilities granted to the top three clusters respectively are 0.271, 0.173 and 0.556.

Tourism, as a clean and high employment generating industry, has been amongst the world’s top income generating industries and received a great deal of attention during the recent decades by development policies and programs.Needless to say, governments and private sectors in at macro and micro levels need to forecast the demand in tourism to survive and develop. Various quantitative and qualitative methods have been suggested in forecasting tourism demand in the literature so far, most of them focusing on quantitative methods, but with the quite recent advent of artificial neural networks and fuzzy neural networks methods, few studies have employed artificial intelligence methods in tourism demand modeling and forecasting.This study intends to forecast Iran’s inbound tourism demand using a suggestive model of fuzzy neural networks and compare its accuracy with ARIMA method. After identification and prioritization of the most significant determinants of Iran’s tourism demand function, and identification of the architecture for the fuzzy neural networks model, the study concludes that thismethod excels over ARIMA in all theperformance forecasting assessment criteria.

Increasing importance of knowledge and growing trend in today's knowledge-based economy, has turned knowledge management into a strategic and vital tool in order to gain and maintain a competitive advantage. And electronic Banks as well as effective organizations in country’s transactions to gain and maintain a competitive advantage, should try to successful implementation of the knowledge management. Because knowledge management can act as a different element in E-commerce environment, and help people to accept their new roles and responsibilities easier and faster in e-commerce environment. Therefore in this research we have focused on the impact of proper infrastructure of knowledge management in E-banking. The method of this research is descriptive-analytical and Mellat Bank of Qom province is selected as the statistical subjects. And to evaluate the reliability, inconsistency rate is calculated. Also To prioritize indicators of Fuzzy Analytic Hierarchy Process is used. As a result, this research indicates that the most important criteria include “IT infrastructure, knowledge infrastructure and organizational infrastructure”.

Today, many provider organizations to survive and surpass other competitors in the world to compete. The new techniques and tools to identify and prioritize the most important metrics are used to obtain the customer's consent. Banks, as a service provider organizations are no exception. Quality key role in the banking industry and customer satisfaction Arzayt and as one of the objectives in this area are considered to be basic. Knowledge of customer needs and requirements to provide satisfactory services to our clients to facilitate them - slow. Can be said to have a better understanding, identification and prioritization of key customer requirements for bank executives in the banking industry will be considered. Expansion of electronic communication and access to the worldwide web a lot of people in the world, suitable for the establishment of trade and economic interaction is provided. This increases competition in the banking industry and has been providing electronic banking. Purpose of this study methods of integrating Quality Function Deployment (QFD), Kano model and fuzzy analytic hierarchy process (FAHP) is. In this study a new method to detect and analyze customer needs, priorities bank offers. The results show that the priority banking customers before and after the integration of the Kano model in the planning matrix of quality home is different.

Knowledge management can act as a different element in E-commerce environment، and help people to accept their new roles and responsibilities easier and faster in e-commerce environment. Successful implementation of knowledge management requires an analysis of the key factors of knowledge management، viable action based on known element. This matter causes better and precise usage of knowledge and achieving beneficial competition in E-banking. Hence، in this study includes descriptive-analytical، we have focused on impact of knowledge management strategies and leadership factor in E-banking، Mellat bank of Qom was chosen to work on this project practically. And for prioritization of criteria is used fuzzy analytic hierarchy method. The results indicate that the three main criteria of «top management support، knowledge management strategy and organizational strategy»، respectively، are of paramount importance.

In this paper, a nonlinear multi objective mathematical model for crashing the PERT network is presented. The main purpose of this model is minimizing the pessimistic time of critical activities by allocating more budget. In fact, this model indicates how the budget is allocated among critical activities. The results show that decreasing the pessimistic of critical activities lead to decreasing the time and variance of project completion. This model increases the probability of project completion on time scheduling. A genetic algorithm is use to solve the nonlinear model.

In today's business world, proper identification of customer’s requirements and a quick response to these requirements is a key to commercial success. Increasing customer loyalty affects the profitability and organizations can ensure their long-term interests by means of planning. In today's competitive world, the services provided by the competing company have to be more similar to each other and can be hard to surprise customers within completely new service in the long term, because the newest services are quickly imitated by competitors and marketed. Hence investment in customer loyalty is an effective and profitable investment for companies. One criticism that has entered into the e-service is customer loyalty. In order to study the causes of e-loyalty for organization providing e-services, this research identified the factors affecting customer loyalty in e-services and with questionnaire prepared and by using fuzzy hierarchical decision-making process acquires the weight of each factor and ultimately rank them. The results show that the quality of service provided to e-services customers is the most important factor in creating e-loyalty.

Fund or capital which is needed to finance a business or invest in a plan is prepared by different groups, those groups expecting a significant rate of return. Cost of equity of a business is equal to the minimum expected returns which a plan should include, and in this case investors will become eager to pay their fund for business. Research shows that various factors have effect on expected rate of return. Basu (1977) mentioned in his studies that those stocks that have larger P/E ratios will have a larger expected return. Bhandari (1988) described in his studies that depth ratio to total equity of shareholders as one of leverage ratios, have a relationship with expected return. Therefore, investors according to financial information published by companies and analyzing them through financial ratios, estimate risk of investment in companies and according to it, they demand specified expected return of their investment. And this rate is what we called cost of investment. Depending on the importance of the mentioned points, this research is representing the relationship between financial ratio and cost of stocks in Tehran Stock Exchange. Hypothesis 1- There is a significant relationship between liquidity ratios and cost of equity 2- There is a significant relationship between profitability ratios and cost of equity 3- There is a significant relationship between net cash flows From operating activities to operation profit Ratio and cost of equity 4- There is a significant relationship between leverage ratios and cost of equity 5- There is a significant relationship between market ratios and cost of equity 6- There is a significant relationship between return of Shareholders equity and cost of equity 7- There is a significant relationship between return of asset and cost of equity In this research we studied the relationship between financial ratios and the cost of equity during the period of 1382 to 1388. A sample of 60 TSE listed companies was selected and tested. Gordon Model was used to calculate equity cost and Multivariate Regression Model was used to test and analyze the hypothesis Results, discussion and The results of this research show that at a confidence level of 95%, there is a significant relationship between liquidity ratios, profitability ratios, leverage ratios, market ratios, and cost of equity.

Today, stock investment has become an important mean of national finance. Apparently, it is significant for investors to estimate the stock price and select the trading chance accurately in advance, which will bring high return to stockholders. In the past, long-term trading processes and many technical analysis methods for stock market were put forward. However, stock market is a nonlinear system, due to the political, economical and psychological impact factors. Thus, it is difficult for us to use traditional analysis tools to make stock transaction decision. With the developmenting of nonlinear methods such as neural networks and fuzzy neural networks, we can now use these methods for stock price forecasting. In this research, we presented three scenarios: 1) stock price forecasting with classical methods approach, 2) stock price forecasting with artificial intelligence methods approach, and 3) stock price forecasting with hybrid model. Therefore, first, we designed classical models such as exponential smoothing, trend analysis, and ARIMA, then artificial intelligent models such as neural networks and fuzzy neural networks were designed, next the third scenario i.e., hybrid model, was presented. Finally, the scenarios were measured. The experimental result showed that the hybrid model had more accuracy than the classical or artificial intelligent models. The hybrid model enjoyed also such properties as fast convergence, high precision and strong function approximation ability, there¬fore, it is suitable for real stock price forecasting.

Among flows in every supply chain (finance, information, and material), material flow according to its part in product cost is very important. Many studies in the field of material flow showed that material flows are often single level. This study offers a comprehensive model for appropriate order assignment in different levels of supply chain according to minimizing related cost to with genetic algorithm method. Results based on genetic algorithm is compared to other common pattern search methods such as Latin Hypercub and Mead-Nelder. The evidence shows that genetic algorithm method is the best method among other methods.

In order to respond effectively to his clients, the National Gas Company needs to know about the amount of prevailing demand in every period. From the other hand, lack of information about the amount of subscription demand will in turn cause certain difficulties such as not knowing the number of required contractors, the absence of an appropriate inventory control for all kinds of needed gas consumption counters, etc.In the last few decades, economists and management specialists have frequently used econometric methods for estimating demand. Today, among the available estimation methods, they are using the artificial neural networks methods and fuzzy models in many areas of applications and each of them have its own merits and limitations. A successful combination of these two methods, with emphasis on learning power of neural networks and logical performance of fuzzy model, have been transformed into a very powerful instrument that are now being applied in different contexts.The main purpose of this article is to estimate the urban household gas subscription demand by using the ARIMA linear method and the fuzzy neural networks nonlinear method and to compare them on the basis of six performance criteria. The article concludes that, considering the six performance criteria, the fuzzy neural networks nonlinear method has supremacy over the ARIMA linear method for estimating the urban household gas subscription demand; therefore, it is more appropriate.