The Impacts of Human Resource Management Practices on Company Labor Productivity: Empirical Evidence from Iron and Steel Company in Libya

This paper investigates the relationship between human resource management practices  and labor productivity for the iron and steel Libyan company. This paper operationally defined human resource practices as recruitment and selection, training and development, performance appraisal, communication, compensation, teamwork and employment security. Also, productivity measured as labor productivity. The data was collected using the questionnaire that consists of questions with 5-points Likert scales distributed to our samples of 386 employees. By using a stepwise multiple regression analysis, it is found that, all practices had strong positive correlate and effect on each of labor productivity

SVC Parameters Optimization Using a Novel Integrated MCDM Approach

Nowadays, multi-criteria decision-making (MCDM) methods are used widely in many fields of research and applications. Many studies have shown that MCDM approaches are effective in determining the optimal solution to a variety of symmetrical and asymmetrical problems with numerous parameters. This article investigates a novel approach using multi criteria decision making (MCDM) to optimize the parameters of static var compensator (SVC) and power system stabilizers (PSS). The proposed technique integrates similarity membership function reduction algorithm (SMFRA), removal effects of criteria (REC) and combined compromise solution (CoCoSo). In the first stage, (SMFRA) is employed to select the most dominant controller parameters in the optimization process. Secondly, the weights of the reduced parameters are computed based on (REC). Finally, (CoCoSo) method searches for the optimal setting parameters. A detailed sensitivity analysis is presented to evaluate the obtained results. It is found that the suggested integrated technique is time saving, easily implemented and of low computation burden, which can successfully be implemented to solve a wide range of issues, both comparable and dissimilar

Optimization of Observer Feedback Gains for Stable Sensorless IM Drives at Very Low Frequencies: A Comparative Study between GA and PSO

Instability of an adaptive flux observer (AFO) in the regenerating mode at low frequencies is a great challenge of sensorless induction motor (SIM) drives. Zero observer feedback gains (OFGs) in the regenerating mode at low frequencies are the main reasons for moving the dominant zero of the speed estimators to the unstable region. OFGs should be appropriately selected to transfer the unstable dominant zero to the stable region. In this paper, genetic algorithm (GA) and particle swarm optimization (PSO) techniques were used to design the OFGs for a stable observer. A fair comparison of the dominant zero location between the two approaches using the optimized OFGs is presented under parameter deviation. Analytical results and the design procedure of the OFGs using the two approaches are presented under deviations of stator resistance and mutual inductance to guarantee a stable dominant zero in the regenerating mode of IM. The dominant zeros obtained by PSO had a superior location to that obtained by GA for both stator resistance and mutual inductance deviations. It was observed that one of the gains had an almost constant value over a wide range of parameter deviations. However, the value of the other gain was dependent on the deviation of machine parameters. The advantage of using PSO over GA is that the relation between the gain and parameter deviation can be represented by a deterministic and mostly linear relationship. Simulation and experimental work of the SIM drive are presented and evaluated under the optimized OFGs

Optimization of Observer Feedback Gains for Stable Sensorless IM Drives at Very Low Frequencies: A Comparative Study between GA and PSO

Instability of an adaptive flux observer (AFO) in the regenerating mode at low frequencies is a great challenge of sensorless induction motor (SIM) drives. Zero observer feedback gains (OFGs) in the regenerating mode at low frequencies are the main reasons for moving the dominant zero of the speed estimators to the unstable region. OFGs should be appropriately selected to transfer the unstable dominant zero to the stable region. In this paper, genetic algorithm (GA) and particle swarm optimization (PSO) techniques were used to design the OFGs for a stable observer. A fair comparison of the dominant zero location between the two approaches using the optimized OFGs is presented under parameter deviation. Analytical results and the design procedure of the OFGs using the two approaches are presented under deviations of stator resistance and mutual inductance to guarantee a stable dominant zero in the regenerating mode of IM. The dominant zeros obtained by PSO had a superior location to that obtained by GA for both stator resistance and mutual inductance deviations. It was observed that one of the gains had an almost constant value over a wide range of parameter deviations. However, the value of the other gain was dependent on the deviation of machine parameters. The advantage of using PSO over GA is that the relation between the gain and parameter deviation can be represented by a deterministic and mostly linear relationship. Simulation and experimental work of the SIM drive are presented and evaluated under the optimized OFGs