درخت حوزه‌های تخصصی

The Iranian economy in recent years is due to the development of economic sanctions, a sharp decline in the price of oil and the deficit resulting from revenue - dependent on oil revenues and the trend towards tax revenues. While comparing the volume of the Iranian economy with the amount of tax income indicates the existence of a relatively significant tax gap. The tax gap is the difference between the collected taxes and the tax required by the law. The purpose of this study is to answer the question whether corporate governance is effective and strong on the relation between tax gap and future earnings changes? The statistical population of the research in Tehran Stock Exchange (TSE) firms and statistical sample consists of 120 companies in the period of 2007-2017. In order to test the hypotheses, multivariate regression using mixed data - data approach has been used. The results indicate that there is a significant inverse relationship between the tax gap and future earnings changes. It can be argued that increasing the difference between earnings accounting earnings can be associated with decreasing interest in the next year and less stability. On the other hand, significant positive relation between corporate governance is efficient and strong with future earnings changes. because corporate governance will ultimately lead to more sustainable future gains due to the decline of discretionary accruals in discretionary accruals. It is also reinforced by the effect of the tax gap on future earnings changes in firms that have efficient corporate governance, and this effect is only seen for a year later. And is not effective for the second and third years.

In this study , a new model of combining variables affecting the classification of customers is introduced which is based on a distribution system of goods and services. Given the problems that the RFM model has in various distribution systems, a new model for resolving these problems is presented. The core of this model is the older RFM. The new model that has been proposed as PRFM, consists of four dimensions: Profit margins (P), time period from customer's last purchase (R), Frequency of transactions (F) and the Monetary Value (M). Adding variable (P) makes a huge change in customer clustering and classification systems and makes it more optimized for future planning. For review and approval, the model was implemented in one of the largest and most diversified distribution companies in Iran. Using Ward's clustering, the optimal number of clusters was prepared and entered by hierarchical clustering and based on Euclidian distance customers are clustered and separated. One of the most important results of this study is introducing a new model and resolving the problems of the old RFM model in determining customer's value.      

The main aim of this study is proposing an assessment model for evaluating the supply chain sustainability across the automotive sector. In this study, through reviewing the sustainability indicators in the economic, social, environmental, and governance pillars, the fuzzy Delphi is applied. Then, survey method is used to implement the model designed in CANFIS to evaluate the sustainability indicators. In this research, the proposed model was used for evaluating the sustainability in four pillars which is more inclusive than previous research. To improve the sustainability, it must be evaluated and measured so that after the improvement measures, the results are determined through measurement. According to the findings, it was concluded that the model designed in CANFIS was a reliable tool for assessing the sustainability .

One of the most necessary operations in humanitarian logistics is the distribution of relief goods to the population in disaster areas. When a disaster occurs, some parts of the distribution infrastructure may be damaged and consequently make it impossible to reach all the demand nodes and delivering the relief goods. In this study, we focus on the planning of infrastructure recovery efforts in post-disaster response. The problem is the scheduling of the emergency repair of a network that has been damaged by a disaster. The objective is to maximize network accessibility for all demand nodes in order to deliver relief goods to them. We adopt a dynamic programming algorithm to solve the problem when more than one crew group is available. Our numerical analysis of the solution shows the performance of the algorithm. We, also, compare our results with some similar studies to indicate the differences between one and multi-crew scheduling.