الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
 |  | from | 112 |  |  |
| | | from | 112 | | |
Abstract
In recent years, call centers have been considered as an integral part of the modern businesses, since they play an important role in providing service delivery functions to their customers. High performance of call centers, therefore, is crucial to ensure high level of customer satisfaction in today’s competitive market. In order to achieve that high performance, managers of call centers face a very difficult set of challenges. At the top level, they must strike a balance between two powerful competing interests where they need to achieve low operating costs and high service quality. On a day-to-day basis, while simultaneously keeping low costs and service quality, those managers are in real need to an accurate performance evaluation and enhancement tools. Such tools play a vital role in understanding the current system performance, evaluation of any proposed enhancement scenarios, and optimizing operations management decisions under any unexpected operating conditions.
In this thesis, one of traditional operations management challenges, for call center managers, is tackled mainly, the multi-period human resources allocation problem. In this regard, the staffing and staff scheduling decisions in single-skill inbound call centers is studied. These decisions are normally made under strict service level constrain in the presence of high uncertain operations and demand of call center services. Neglecting such uncertainty leads to unrealistic decisions. The objective of the current work is to propose a framework to enhance the call Center performance through obtaining realistic optimal staffmg and scheduling decisions. Realistic optimization requires realistic modeling (evaluation) of call center operations which is the main contribution of the current work.
The proposed framework combines statistical, simulation, and integer programming (JP) techniques in achieving realistic optimality. The framework begins by developing stochastic statistical data models for call center operations parameters which are divided into service demand (amval volumes) and service quality (service times, abandonment volumes, and patience time) parameters. These data models are then used to run a simulation model that is used to determine the minimum staffing levels in daily an-hour periods. Finally, these staffing levels are considered as input to an JP model that optimally allocates the service agents to the different operating shifts of the working day. An application of proposed framework to a large call center in Egypt is presented and improves the staffing and scheduling decisions in that call center.