In today's competitive business, it becomes increasingly important to provide accessibility to the company, which has a direct impact on the customers' perception of quality, in order to acquire, keep, and grow customers [8,58,123]. A contact center provides such accessibility for customers who buy or reserve products or services and for customers who have questions, complaints, or need for technical supports and other customer services. Although a contact center is a significant revenue generator, its operating expenses are also huge, and human resource costs are estimated to account for 50-75% of the total operating expenses [58,123].
Thus, a major challenge in contact centers is determining the number of agents to be assigned (capacity planning) such that a right level of customer satisfaction is achieved at right operational cost. Designing good routing policies (that determine which customer is served by which agent) is also important, since good routing policies can increase the accessibility or shorten the waiting time without increasing the number of agents. As a result, a significant amount of research has addressed analytical tools and simulation techniques that support capacity planning and routing policy design at contact centers [119].
However, operations at contact centers have become increasingly complex, and the increased complexity makes it difficult to apply existing analytical tools to capacity planning and routing policy design at today's contact centers. We will discuss specifically how the analytical tools developed and lessons learned in this thesis may help in capacity planning and routing policy design at contact centers today.