Some estimates put the number of people in the U.S. that work in call centers as high as 3 million. This is consistent with the well-known shift of the U.S. economy from a manufacturing base to a service base. The proposed research is to study the dynamic control of service systems with three major innovations: upgrades, reneging, and retrials. Although the motivation in each case stems from call centers, the results obtained will be applicable more generally. The following questions are to be considered:
? After market segmentation, are there simple modifications to classic resource allocation policies that can reduce waiting times for lower class customers, while guaranteeing high quality service for higher class customers? ? Do the insights drawn from classic allocation policies continue to be useful in the advanced systems of today? ? In models with controlled the admission rates, are there simple, implementable policies given today's new decision-making paradigms?
To tackle each of these questions specific models of service systems are presented. Each model presents new challenges from both a theoretical and managerial standpoint. If successful, insights into optimal resource allocation in service systems will be provided as well as opening the door for new exploration of service system control.
The intellectual merits of our proposed research include examining extensions of some of the classic models in scheduling and admission control. These extensions stand to update the state of the art to cover more recent innovations in service systems and answer new paradigms introduced in the current service economy of the United States. In some cases we expect that the insights gained can serve as a warning against blindly using classic results as the models become more intricate. Indeed, the proposed models are not even proposed the most general possible, yet the insights even so far are profound. The author continues to work vigorously to pursue top Minority graduate students to be involved in his work. Moreover, the results will enhance classroom instruction for advanced courses in applied probability and operations research/management that the author teaches at Cornell University.
