Performance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling

Approximate Dynamic Programming by Linear Programming for Stochastic Scheduling

In stochastic scheduling, we want to allocate a limited amount of resources to a set of jobs that need to be serviced. Unlike in deterministic scheduling, however, the parameters of the system may be stochastic. For example, the time it takes to process a job may be subject to random fluctuations. Stochastic scheduling problems occur in a variety of practical situations, such as manufacturing, ...

The application of Approximate Dynamic Programming techniques

Stochastic scheduling with event-based dynamic programming

In this paper we apply a new framework for the study of monotonicity in queueing systems to stochastic scheduling models. This allows us a unified treatment of many different models, among which are multiple and single server models (with and without feedback), discrete and continuous time models, models with controlled and uncontrolled arrivals, etc.

Scheduling Policy Design using Stochastic Dynamic Programming

OF THE DISSERTATION Scheduling Policy Design using Stochastic Dynamic Programming by Robert Glaubius Doctor of Philosophy in Computer Science Washington University in St. Louis, 2009 Research Advisor: Professor William D. Smart Scheduling policies for open soft real-time systems must be able to balance the competing concerns of meeting their objectives under exceptional conditions while achievi...

Sensor Scheduling for Target Tracking Using Approximate Dynamic Programming

To trade off tracking accuracy and interception risk in a multi-sensor multi-target tracking context, we study the sensor-scheduling problem where we aim to assign sensors to observe targets over time. Our problem is formulated as a partially observable Markov decision process, and this formulation is applied to develop a non-myopic sensor-scheduling scheme. We resort to extended Kalman filteri...