Combining Queueing Theory and Scheduling for Dynamic Real World Systems

The central thesis of my dissertation is that stochastic reasoning from queueing theory can be integrated with combinatorial scheduling to provide high quality sched-ules for real world, dynamic systems. This thesis builds on work that showed the potential of such a combination for dynamic systems found in the queueing and schedul-ing literature. My study strives to extend this concept to handling real world problem areas which are often very time sensitive and restrictive in available system con-trol and information. In such systems, I believe profi-cient reasoning about system dynamics and combinato-rial optimization is essential for creating schedules that are robust and have desirable long-run performance. I will study the performance improvements possible by integration as well as the effects of control, time, and information restrictions of a system