This paper presents an empirical investigation to find the important factors influencing implementation of knowledge management in revenue agencies in Iran. The proposed study designs a questionnaire and distributes it among some experts and, using principal component analysis, the study detects important barriers for knowledge management implications. The study selects 170 people from 880 regular employees who work for revenue agency and distributes the questionnaire designed in Likert scale. Cronbach alpha has been determined as 0.70, which is well above the minimum acceptable level. In terms of knowledge management, the study determines six factors including support for creativity and innovation, knowledge sharing, trust, employee promotion plan, mission and training programs and they represent 66.57% of the described factors. In addition, in terms of barriers, job security, cultural issues, individual differences, infrastructure, organizational structure and lack of vision are the most important issues representing 61.98% of the factors.

Oxidation of alcohols to carbonyl compounds has become an important issue in the process industry as well as many other applications. In this method, various benzylic alcohols were successfully converted to corresponding aldehydes and ketones under transition metal-free condition using hydrogen peroxide in the presence of some amount of catalytic acidic silica gel. Silica gel is inexpensive and available. One of the most important features of this method is its short reaction time.

This paper presents a review of human resource planning methods, related techniques, and their effects on cellular manufacturing systems (CMS). In-depth analysis has been conducted through a review of 43 dominant research papers available in the literature. The advantages, limitations, and drawbacks of material transferring methods have been discussed as well. The domains of the examined studies include some of the important problems in staff planning, such as worker assigning, hiring and firing, optimum number of workers, skilled workers, cross-functional experts, worker satisfaction and outsourcing. The results of this study can fill research gaps and clarify many related questions in CMS problems.

This paper presents an empirical investigation to study the relationship between employee performance and TQM. The proposed study of this paper designs two questionnaires for TQM and performance measurement and distributes them among some employees who worked for one of Iranian banks in city of Semnan, Iran. The result of Kolmogorov–Smirnov test confirms that all data are normally distributed and the study uses Pearson correlation to investigate the relationship between various components of the survey. The result of the implementation of Pearson correlation ratio indicates that there was a positive and meaningful relationship between employee performance and TQM components (r=4.6223, P-value=0.000). In addition, there are some positive and meaningful relationships between TQM components and employee performance. The highest correlation belongs to relationship between employee performance and feedback (r=4.6223, P-value=0.000) followed by training and development (r = 0.441, P-value = 0.000) and communication (r = 4.2861, P-value = 0.000).

Loading and unloading operations for Stock Keeping Units (SKUs) in warehouses are critical to logistics management systems. However, these operations also have a significant impact on the environment, particularly in terms of carbon dioxide (CO2) emissions. As such, warehouse management must consider not only operational efficiency but also environmental impact. The reduction of CO2 emissions in warehouses is becoming increasingly important, both for legal compliance and to meet sustainability targets. In this article, we will emphasize the environmental impact of warehouse operations, particularly on CO2 emissions, and explore ways to minimize them while still maximizing warehouse performance. We will review various optimization models proposed to address this issue and highlight the importance of considering environmental objectives when designing warehouse operations. We will also describe a simulation study conducted to determine the Pareto optimal frontier for a warehouse design, considering transportation, space utilization, and CO2 emissions. The outcomes of implementing this simulation's results include reduced CO2 emissions and increased space utilization, which demonstrate the potential benefits of considering environmental objectives in warehouse design and management.

This paper investigates credit allocation policy making and its effect on economic development using bi-level programming. There are two challenging problems in bi-level credit allocation; at the first level government/public related institutes must allocate the credit strategically concerning sustainable development to regions and industrial sectors. At the second level, there are agent banks, which should allocate the credit tactically to individual applicants based on their own profitability and risk using their credit scoring models. There is a conflict of interest between these two stakeholders but the cooperation is inevitable. In this paper, a new bi-level programming formulation of the leader-follower game in association with sustainable development theory in the first level and data mining classifier at the second level is used to mathematically model the problem. The model is applied to a national development fund (NDF) as a government related organization and one of its agent banks. A new algorithm called Bi-level Genetic fuzzy apriori Algorithm (BGFAA) is introduced to solve the bilateral model. Experimental results are presented and compared with a unilateral policy making scenario by the leader. Findings show that although the objective functions of the leader are worse in the bilateral scenario but agent banks collaboration is attracted and guaranteed.

The movement of industrial societies towards implementation of information technology has begun and it has gained moment since early 90th and presently the speed of this movement is well obvious in less developed countries, including Iran. Internet Banking is one of the most tangible trends in today’s electronic commerce. It has direct relationship with the perception of the service qualities not only because of unique absorption of the internet, but also because of e-commerce, acceptance of internet services of banks by clients and the tendency for using such services. This paper investigates different factors influencing on acceptance of electronic banking in private banks versus public banks in a case study of two Iranian banks namely; Eqtesade Novin and Sepah using Technology Acceptance Model (TAM). Correlation test reveals that there was a positive and significant relationship between clients of private and public bank on five factors associated with TAM. In addition, structural equation method was used for investigation of relationships in offered model. In conclusion, all primary and secondary hypotheses for clients of private and public bank were confirmed.

Cash flow models are one of the spotlights for evaluating a project. The actual data should be modeled then it could be used for the prediction process. In this paper, 996 airplane maintenance basis data are used as a database, and 119 similar data are chosen after clustering. The project is divided into 20 equal periods and first three periods are used for simulating the next point. The predicted data for each point is achieved by using of previous points from the beginning. The model is based on artificial neural network, and it is trained by three algorithms which are Genet-ic Algorithm (GA), Estimation of Distribution Algorithm (EDA), and hybrid GA-EDA method. Two dynamic ratios are used which are dividing the population into two halves, and the other is a ratio without dividing. The ratio would give a proportion to GA and EDA models in the hybrid algorithm, and then the hybrid algorithm could model the system more accurately. For each algorithm, three main errors are calculated which are mean absolute percentage error (MAPE), mean square error (MSE), and root means square error (RMSE). The best result is achieved for hybrid GA-EDA model without dividing the population and the MAPE, RMSE, and MSE values are %0.022, 28944.59 Dollars, and 837789503.79 Dollars, respectively.

Decision support system (DSS) normally consists of a set of related computer programs and the data required to help with analysis and decision-making within an organization. Many software vendors claim their decision support, analytics or business intelligence applications may provide a competitive advantage by providing different facilities such as business intelligence tools, business performance management software, data mining tools, etc. This paper presents an empirical investigation to determine different DSS factors influencing on development of export activities on selected firms which are active in Tehran Stock Exchange. The study designs a questionnaire consists of 35 questions and distributes it among 211 randomly selected managers who were involved in export activities. Cronbach alpha has been calculated as 0.817, which is well above the minimum desirable level. Using principle component analysis, the study has detected five factors including internal resources, management approach, quality of data, efficiency of data and organizational approach, which influence the most on export activities.

Investment decisions are normally accomplished based on fundamental or technical methods. However, there are many cases where investors make their investment decisions based on their emotions. This study investigates the effects of various factors including biases representation, mental accounting and risk aversion when an investment decision is executed. In other words, the study examines the effects of emotional intelligence components on retail investors’ investment strategies on Tehran Stock Exchange (TSE). The proposed study selects a sample of 270 investors who had some experiences on TSE randomly and using a questionnaire based survey detected that there was a positive and meaningful relationship between emotional intelligence and investment decisions.