فهرست مطالب seyed hossein razavi hajiagha

According to economic researchers, tax is the most appropriate and stable type of income for the government and an efficient tool for execution economic policies. Therefore, from their point of view, for the continuation of government activities, an efficient tax system is necessary. One of the factors that is very important in establishing an accurate and effective tax system, especially in developing countries, is the tax behavior of taxpayers. The purpose of this research is identifying effective factors on taxpayer’s tax behavior and the structure of relationships between them. For this purpose, mixed methodology is used. Factors have been identified by content analysis, the structure of relationships between them has been identified by interpretive structural modeling, and they have been classified by MICMAC analysis. The results show that taxpayers' financial behavior, transparency in tax collection processes, tax rates and coefficients, unions and trade associations, mass communication tools, tax laws and regulations, the attitude of senior tax managers towards tax culture, designing and establishment a comprehensive tax system and increasing people's awareness are the most important effective factors on taxpayer’s tax behavior

One of the important issues in project management is project scheduling. Since projects are faced with high uncertainty, project scheduling is of high importance under uncertain conditions. The purpose of this research is to present a robust multi-objective model to optimize project scheduling with limited resources by considering uncertainty, in which activities have several execution modes with uncertain duration, costs, and resources.
After reviewing the extant theoretical literature on project scheduling, the assumptions, parameters, and variables of the mathematical model were determined. Then, considering the goals and limitations of the project scheduling problem in deterministic conditions, a mathematical model was developed. This model was transformed into a single-objective model by the epsilon constraint method. To consider the uncertainty in the parameters of the problem, robust optimization, and Bertsimas and Sim's approach were used. Also, Robust optimization of the single-objective model was developed to consider the uncertainty.
According to the obtained results, while the duration of the project increases with the increase in the tolerance of non-deterministic parameters; the percentage of project duration changes decreases for higher values ​​of robust parameters.
The robust parameters are with negative coefficient in the objective function, so their increase leads to a decrease in the objective function value. The highest reduction of the objective function is when the robust parameter is changed from two to one. This coefficient decreases with the increasing value of the corresponding parameter i.e., as the value of stability parameters increases, its effect on changing the value of the objective function decreases. The cost parameter was changed between -40% to +40% for the value of gamma 10 (the state where 10 activities of the project are non-deterministic) for different variation values of parameters of non-deterministic activities. Its effect on the values of the objective function indicated that the variation of the cost parameter in the range of 0 to +40% turns it into an unnecessary constraint and that its change has no effect on the value of the objective function (project duration). Also, in the range of 0 to -40%, the cost reduction caused a decrease in the value of the objective function (increasing the project duration) and the maximum impact of the reduction of the project budget related to the situation where the uncertain parameters of time and cost change by 40% and 50%.

One of the major problems of the banking system in recent years is the creation of non-current claims. In fact, a bank's operating profit, which includes the profit received for granting facilities to customers, cannot be realized due to the creation of bank claims, and the circulation of money and facilities becomes stagnant. Therefore, in order to adjust bank claims in the future and, considering interest rates, to estimate the profit function of bank customers using the customer lifetime value improved with Markov chain, the researchers present a model. Since there are eight different types of facilities for customers in the researched branches of Mellat Bank, information was obtained separately for each of these facilities, including the profit and customers’ claims, and for each loan a function was obtained. As such, each of these facilities had a specific function and a specific profit and loss for the bank. After obtaining the profit and loss of each one, for validation purposes, the researchers compared them with the previously obtained, as well as settled, facilities and analyzed the results.

Improving the responsiveness to customers’ orders is the goal of this research. Balancing the manual and automated production lines using proper equipment is crucial for the case study to reduce production costs and increase the quantity. The main problem in this research is responding to orders and demand using the proper equipment and machines in the production line. Using automated equipment can improve productivity and responses, but using advanced technology is not necessarily effective and should be organized in proportion to demands. As in this case study, there are complex cause-and-effects relations; using System Dynamics is very effective. Different productivity factors, delays, orders, etc., are considered to determine the proper production method and equipment in the long term. After that, behavioral reproduction and extreme condition testing validate the model and compare the automated production line equipment percentage usage. According to the volume of orders, the optimum case is suggested. Then, different scenarios related to customer volume are analysed, and finally, the policies are examined. As the simulation results in current conditions and market forecasting indicate, 67.2% of production line equipment should be automated. Any customer order changes can increase or decrease the results, which should be calculated accurately. Considering all the influential factors, the presented model helps the production managers have the best policy for choosing the production method. Using this method, considering some changes in the variables can be used in different industries as optimizing the production line equipment is the main key finding of this research.

Energy saving regarding its high share in energy consumption of industries has a significant impact on the growth and development of countries. This study aims to evaluate the performance of Iran's electricity generation, transmission, and distribution processes.

By using a network data envelopment analysis (DEA) model, the overall efficiency scores, and efficiency scores of production, transmission, and distribution processes are calculated. The network structure considers the main and surplus inputs (fuel consumption costs, internal consumption, transmission substation capacity, power transmission lines length, transformers capacity, low and medium voltage network length), intermediate sizes (net power generation, gross power generation, and delivered energy), desirable (nominal power, actual power, and delivered energy) and undesirable outputs (environmental pollutants, and energy losses).

An algorithm based on a multi-objective programming model is presented to evaluate network performance and simultaneously to evaluate processes efficiency. The proposed algorithm is used to evaluate 16 electricity areas in Iran.

The results showed that Tehran, Khorasan, Khuzestan, and Zanjan are the most efficient areas.

Proper management and optimal allocation of financial resources will increase gross national product and growth, create jobs and increase public welfare. The purpose of this study is to present an investment strategy that has tried to pave the way for the development of the investing company in the financial markets. Therefore, the forthcoming research can be considered as applied in terms of purpose. Also, considering that in the present research, mathematical modeling, modeling, artificial intelligence, etc. are used and the optimization of the investor company's portfolio is evaluated with the proposed model, so it is a quantitative and descriptive research. This study evaluated the performance of the proposed model in three modes: prudent, moderate and risky investor company. The results showed that for all three cases, the proposed strategy performs significantly better than the market index and other previous strategies. At the end of the investment period, the risky portfolio was more valuable than other portfolios. On the other hand, a prudent portfolio has achieved a more stable and stable return. These results revealed that the proposed fuzzy programming is able to reflect the characteristics and desires of the investor company in the portfolio composition.

Regarding urban population growth and importance of time and cost in the societies of today, subway systems have faced with demand increase and passengers waiting time have prolonged. In urban transportation systems especially in subways, the passengers waiting time influences the society satisfaction deeply. Since the emersion of rail network, time and scheduling as important challenges have been emerged. Then the periodic train planning as one of the best solutions was introduced to decrease passenger waiting time, reduce travel length, and increase transportation safety. This challenge should be solved by subway managers. Due to the limited number of trains and equipment on subway lines and increase of their cost-effectiveness in reducing passenger waiting times at intersections, an attempt has been made in this paper to reduce waiting time using optimization models that often do not impose costs on the system. In addition, another aim of this paper is to fill the existing research gap in this area.

 This paper aims to calculate the weights of intersectionsstations in order to minimize the passenger waiting time through goal programming (Khalili Damghani et al. 2015; Mosadeghi et al. 2010). Many studies have been done on train scheduling and transportation mathematical modelling all around the world (Moghaddam and Mahlooji, 2017; Sadeghieh and Dehnavi-Arani, 2019). The aim of this paper is to minimize the deviation from desirable situation through goal programming. The main function of the proposed model is formulated again along with new construction, auxiliary variables and optimum values. It consists of variables with the concept of “deviation of desirable situation”. In this paper, both of the deviations of desirable situations are shown by  and    which are undesired and should be minimized to achieve the optimum value. For this purpose, the fourth line of Tehran subway system and its intersectionssections are considered as the real world problem.

The model is solved by Lingo and optimum values are calculated. The model is developed based on goal programming and it can reduce the passenger waiting time and satisfaction which results in increased acceptance of public transportation in society. These findings are compared with current values of time tables for first and last movements. Findings indicated that the proposed model can reduce the passenger current waiting time by 42%.

In this paper, the accurate amount of passenger population was determined in a day based on lines, separately. Also, it was difficult to collect study data from the Tehran subway system organization. The proposed approach resulted in reduced passenger waiting time. Suggestions for further study include examining and analyzing the factors causing train delay and comparing the results of each single line with another; using simulation method to develop an optimization model for time tabling of train movement in order to optimize energy consumption; calculating and optimizing the number of subway station staff; and optimizing models to design the subway station network.

 As Tehran public transportation system is extending to cover more urban regions, the intersection stations will increase. Therefore, the role of these stations will be more tangible. Each intersection station scheduling affects the train movements in other stations and this continues throughout the length of the network. It means that each delay and pause will influence the other intersections, increasingly. The subway system managers can employ this model on other lines and reduce the waiting time of passengers in the whole Tehran subway.

As mentioned earlier, the waiting time of passengers in different stations, especially in intersections, is an important factor in using subway system as an effective public transportation system. It will increase or decrease the rate of subway usage. More public transportation usage leads to less air pollution, less traffic and faster travels in the city, which are all desirable for society.

In this paper, a goal programming model was proposed to minimize the deviations in the objective function and subsequently, the passenger waiting time in intersection stations. As the intersection stations are increased, the role of the proposed method can be considered as a solution for public transportation in different cities of Iran.

Master production scheduling is an effective phase of production planning which leads to scheduling and magnitude of different products production in a company. This problem requires investigating a wide range of parameters, regarding demand, manufacturing resource usage and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty, in which demands, as time-dependent variables, are considered as stochastic variables, while cost and utilization parameters, with cognitive ambiguity, are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a practical problem of a polyethylene pipe and fitting Co. in Iran. The result showed a high degree of applicability.

a lot of studies have been done about customer credit scoring, considering importance of the topic on credit institutions decision making. As an evolutionary computation method, Genetic algorithm is one of the methods used in this field. A variety of papers are published on comparing the performance of genetic algorithms with other scoring method but there is little information regard to fitness functions while these fitness functions play a vital role in overall performance of the model. To further investigation of the problem, three different fitness functions are proposed in the current paper and their performance is compared with other scoring methods including logistic regression and data envelopment analysis. The obtained results have shown that genetic algorithms quadratic function totally outperformed other methods based on accuracy, detection and sensitivity criteria.

Data envelopment analysis operates as a tool for appraising the relative efficiency of a set of homogenous decision making units. This methodology is applied widely in different contexts. Regarding to its logic, DEA allows each DMU to take its optimal weight in comparison with other DMUs while a similar condition is considered for other units. This feature is a bilabial characteristic which optimizes the performance of units in one hand. This flexibility on the other hand threats the comparability of different units because different weighting schemes are used for different DMUs. This paper proposes a unified model for determination of a common set of weights to calculate DMUs efficiency. This model is developed based on a multi objective fractional linear programming model that considers the original DEA's results as ideal solution and seeks a set of common weights that rank the DMUs and increase the model's discrimination power. Comparison of the proposed method with some of the previously presented models has shown its advantages as a DMUs ranking model.

Master production scheduling is a midterm phase in planning which translates the long term aggregate production planning to a plan which determines the scheduling and magnitude of different products production. This problem requires investigating a wide range of parameters about demand, manufacturing resource usage and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty where demands are considered as stochastic variables, while cost and utilization parameters are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a numerical example.

Inventory control is an important subject in supply chain management. In this paper، a three echelon production، distribution، inventory system composed of one producer، two wholesalers and a set of retailers has been considered. Costumers'' demands follow a compound Poisson process and the inventory policy is a kind of continuous review (R، Q). In this paper، regarding the standard cost structure in an inventory model، the cost function of system has been approximated using Response Surface Methodology as a combination of designed experiments، simulation، regression analysis and optimization. The proposed methodology in this paper can be applied as a novel method in optimization of inventory policy of supply chains. Also، the joint optimization of inventory parameters، including reorder point and batch order size، is another advantage of the proposed methodology.

The development of Multiple Criteria Decision Making Techniques is to provide the possibility of using multiple criteria in decision making problems. Multi-criteria decision making problems primarily relate to choosing the best option among several available options and according to some indicators. One of the major issues in this regard is to determine the importance weights of decision criteria. Various methods such as Entropy, LINMAP and Saaty’s method have been presented for determining the weights of the indicators. In this paper, a non-linear model based on the logic of TOPSIS method is proposed to estimate the ideal weights. Simplicity, Reduction of received data from the decision makers in the evaluation process, and the possibility of considering his comments about the preference of indicators to each other can be cited as the advantages of this method.

Inventory control is one of the most important issues in supply chain management. In this paper, a three-echelon production, distribution, inventory system composed of one producer, a set of wholesalers and retailers is considered. Costumer's demands can be approximated by a normal distribution and the inventory policy is a kind of continuous review (R, Q). In this paper, a model based on standard cost structure of inventory systems is developed and a heuristic algorithm is designed to optimize the developed model. The application of model is examined in a series of designed experiments that are compared with simulation results. These comparisons verify the validity of the model. Regarding to real complexities in three-echelon systems analysis, the proposed method can have a wide application in practical problems with the same considerations and assumptions. In addition, this method can be used to approximate those systems that follow a Poisson demand.