There is a universal consensus that improving employee performance is a cornerstone for any organization’s path to success and prosperity. However, when it comes to identifying what factors trigger such an improvement, there is anything but consensus among researchers. In this paper, a sample of government employees in the Sultanate of Oman is studied to discern the factors that affect employee performance. A factor analysis is applied to validate the constructs used to measure the various variables. A correlation analysis and a corresponding multiple linear regression is carried out to test the hypotheses. In contrast to many research findings, the results indicate that the only significant factor is technology; whereas leadership and organizational structure are neutral. The results are analyzed and discussed in view of the socio-cultural context of the Sultanate of Oman and the GCC region.
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Training is a key task for ensuring that employees remain at a skill level that is conducive to productivity and to achieving the organizational objectives. However, training is a very costly investment. Hence, the resources allocated to training must be used wisely to balance the available resources to the goals sought. In this paper, a typical training decision is considered where the management needs to decide on assigning employees at various levels of the organization to various training programs. This is to be done with minimal interruption of the organizations operations while making sure that the different types of employees are assigned to training programs most related to their jobs. Additionally, there is a limit on the capacity of the training facility and the number of trainers which in turn limits the number of training courses that can be offered in any given week. A linear programming model is developed to determine the optimal number of trainees that should be chosen from each department and the optimal allocation of these trainees to the various training courses. The model is applied to a real life situation and the results show that there is an optimal allocation that meets and in most cases exceeds the desired objectives of management while maximizing the utilization of the training resources.
Stochastic dynamic programs suffer from the so called curse of dimensionality whereby the number of evaluations grows exponentially as the number of stages increases. This curse is further magnified when the stochastic variable is discretized as this adds another dimension to the number of evaluations required. As a result, it is computationally infeasible (in some cases outright impractical) to try solving these problems using an exhaustive search over all the parameters. Computational algorithms that combine decomposition techniques and simulation with reasonable assumptions on the stochastic variables can render very efficient solutions with a high degree of accuracy. This paper develops and tests such an algorithm to solve a large scale stochastic dynamic program. The algorithm's performance is tested through a simulation study of a system with the prescribed parameters determined by the algorithm. The results analyzed show that the algorithm generates very good solutions as validated by the performance indicators of the system. The solutions provide the managers of the system with insights into the complex relationships that characterize the system.
In this paper, a linear programming model for optimizing the fleet size and mix for a rental car company is developed and solved. Rental car companies depend on their fleet of vehicles for generating the entirety of their income. Additionally, the investments required are typically very significant due to the high cost of vehicles. Consequently, the composition of the fleet could significantly affect the company’s profitability and sustainability in a volatile demand environment. Determining the optimal fleet size and mix has been the focus of research in particular in revenue and yield management and VRP streams. However, most models focused on cost minimization without taking into account the resale value of vehicles once retired from the fleet. This paper addresses the problem from a return maximization perspective while taking into account resale values of vehicles. Sensitivity analysis is carried out to gain further insight into the problem and enable the model to support the company’s management in refining the strategic plan.
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