محمد مهدی شهبازی

The success or failure of any organization is directly linked to the quality of its human resources selection (recruitment, measurement, and selection). By reviewing the data of a knowledge job, this paper aims to help improve the selection process of that job. Consequently, the selection of appropriate employees’ rate will increase, and the rate of human resource turnover will decrease. The approach of this paper is Applied Research and the strategy is Case Study. This paper combines two computational techniques (DEA and CART). Data Envelopment Analysis (DEA) is a non-parametric technique that determines the efficiency of individuals, but it does not provide information on the details of factors affecting performance (especially non-numerical factors). In the present study, this deficiency has been resolved using the Classification and Regression Tree (CART) (as a data mining technique). The result of this study has provided a framework for combining DEA and CART in order to discover rules on the recruitment of knowledge workers in a specific job (a knowledge job) and in a specific organization (HFJ Institute). The results indicate that ‘work experience’, ‘average score in the last degree’ and ‘age’ are related to the employee performance, and therefore it is necessary to be considered in the process of future recruitment of that job.
By reviewing the data of a knowledge job, this paper aims to help improve the selection process of that job. Consequently, the selection of appropriate employees’ rate will increase, and the rate of human resource turnover will decrease. The approach of this paper is Applied Research and the strategy is Case Study.
In the literature review section, the definition of recruitment and selection (Azar et al. 2013), the definition of knowledge workers (Drucker 1994; Horwitz et al. 2006; Li et al. 2015), tasks of human resource management (Osman et al. 2011) and a background of the use of data mining in the field of human resources (Hajiheydari et al. 2017) have been reviewed.
This paper combines two computational techniques (DEA and CART). Data Envelopment Analysis (DEA) is a non-parametric technique that determines the efficiency of individuals, but it does not provide information on the details of factors affecting performance (especially non-numerical factors). In the present study, this deficiency has been resolved using the Classification and Regression Tree (CART) (as a data mining technique).
  In this research, we tried to develop the previous models and present a new model.
The result of this study has provided a framework for combining DEA and CART in order to discover rules on the recruitment of knowledge workers in a specific job (knowledge job) and in a specific organization (Hedayat-e Farhighteghgan-e Javan (HFJ) Institute). The combination of data envelopment analysis and data mining approaches (and considering qualitative and implicit variables in the estimation of efficiency) is one of the most important innovations in this research.
In the proposed framework, organizations can identify and recruit talents and appropriate individuals in a short time based on data mining and discovery of success patterns (resulted from their past experiences). This action avoids costs of frequent recruitment, and decreases turnover rate and improves performance.
By analyzing the outputs of the designed model for the stage of recruitment and selection in this specific job (a knowledge job) and specific organization (HFJ Institute), six rules were extracted, and based on that, suggestions were given.
At the stage of recruitment, it is better for this organization to take into consideration these rules (the status of jobseekers in ‘work experience’, ‘average score in the last degree’, and ‘age’) and then to decrease costs and failure rate of the recruitment process.
The results are somewhat consistent with the results of previous studies. The proposed approach can be planned and implemented in various jobs and organizations to extract specific rules for these jobs and organizations in order to increase productivity in the process of human resource selection and recruitment.
References
Zhu, X., Seaver, W., Sawhney, R., Ji, S., Holt, B., Sanil, G. B., & Upreti, G. (2017). Employee turnover forecasting for human resource management based on time series analysis. Journal of Applied Statistics, 44 (8), 1421-1440.
Lukovac, V., Pamučar, D., Popović, M., & Đorović, B. (2017). Portfolio model for analyzing human resources: An approach based on neuro-fuzzy modeling and the simulated annealing algorithm. Expert Systems with Applications, 90, 318-331.
Osman, I. H., Berbary, L. N., Sidani, Y., Al-Ayoubi, B., & Emrouznejad, A. (2011). Data envelopment analysis model for the appraisal and relative performance evaluation of nurses at an intensive care unit. Journal of medical systems, 35 (5), 1039-1062.

Nowadays, the focus of industries is changed from efficiency of workers to efficiency of knowledge workers. Thus, the performance appraisal process of the knowledge workers is important. A basic drawback of previous methods for performance evaluation is their biased results due to influence of rater's personal motivations. In this study, by developing a Result Base Method (RBM) approach, the criteria of performance appraisal of knowledge workers in outsourcing conditions is identified in Hedayat-e-Farhikhtegan-e-Javan Institution (HFJ). Different models of data envelopment analysis (DEA) are validated on 25 knowledge workers working at the research and development department at the institution. The DEA was able to classify knowledge workers into efficient and inefficient ones. Based on project career development plans, a set of efficient knowledge workers was used to establish an internal best practice benchmark for improving the performance of other inefficient knowledge workers. The findings show that 4 knowledge workers are efficient. Our findings indicate that the non-radial model of data envelopment analysis owns more separation power comparing with other radial models.

The present paper focuses on study of parameters such as lateral force, wagon weight, train speed and those have greatest effects on wear mechanism of wheel and rail. ADAMS-RAIL simulation software was used for dynamical modeling of train while moving via railway. Also, the effects of mentioned parameters were contributed in this simulation. The results shown on curved rail train with weight gain, from least to greatest weight, i.e. 37.5 tons to 61 tons at the same speed, lateral force is significantly increased to 55% but By studying changes in speed can be realized, which increases the speed of the lowest to the highest speed at 25 kilometers per hour to 65 kilometers per hour (equivalent to 7 meters per second to 18 m/s) is about 15 percent less impact on the increase lateral force on the wheel flange and gauge corner of rail . The effect of speed on the lateral force on the wheel flange and gauge corner of the minimum and maximum crest rail at almost half the weight of the train in the same state.

According to an increasing competition in the current business environment, Strategic Change towards Entrepreneurial Behavior has become a necessity for many organizations. Because organizational culture affects all organizational processes and strategies, thus organizations - with strategic thinking - resort to create cultural sub-structures to facilitate the occurrence of entrepreneurial behavior. The primary purpose of this study is investigation of relative importance of organizational culture dimensions in Strategic Change towards Entrepreneurial Behavior. Attaining the mentioned purpose, by employing a systematic and methodical technique, "Organizational Culture Index (OCI)" and "Entrepreneurial Behavior Scale (EBS)" have been chosen as the most appropriate theoretical framework for culture and entrepreneurship respectively; and in the end, these frameworks have been combined and presented in a complex network. In order to achieve the purpose of this study, capabilities of two techniques including "Focus Group (FG)" and "Analytical Network Process (ANP)" are employed. Regarding this research strategy, "Case Study", the scope of this study is limited to "An Active Organization in ICT Industry ". Results indicate that among dimensions of organizational culture, "trust" and "Innovation" play the most important roles in the development of entrepreneurial behavior.

According to increasing competition in the current business environment, the entrepreneurial environment in the organization has become a necessity for many organizations. Because organizational culture affects all organizational processes, thus organizations resort to create cultural sub-structures to facilitate occurrence of entrepreneurial behavior. The primary purpose of this study is investigation of relative importance of organizational culture dimensions in entrepreneurial behavior occurrence. Attaining the mentioned purpose, by employing a systematic and methodical selection, "Organizational Culture Index (OCI)" and "Entrepreneurial Behavior Scale (EBS)" have been chosen as the most appropriate theoretical framework for culture and entrepreneurship respectively; and in the end, these frameworks have been combined and presented in a complex network. In order to achieve the purpose of this study, capabilities of two techniques including "Focus Group (FG)" and "Analytical Network Process (ANP)" are employed. Results in the case study indicate that among dimensions of organizational culture, "social cohesion" and "trust" play the most important roles in development of entrepreneurial behavior.

High fidelity models are becoming increasingly common in engineering optimization. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. The metamodels are initially developed as surrogates of the expensive simulation process in order to improve the overall computation efficiency. This work presents a new multilevel optimization approach for multidisciplinary structural design optimization based on multi fidelity modeling to decrease computational effort. Such method is a composition of a statistical estimating method and a metaheuristic algorithm. A low fidelity analysis response determines if the high fidelity analysis should be done or not. As a result, most of unnecessary high fidelity calculation will be omitted. The empirical results show the new algorithm causes a significant decrease in computational load as well as increase in convergence rate. Keywords: Multi level optimization; Metamodeling; Harmony search algorithm; Inverse distance weighting model.