No abstract

No abstract

We present a method for dynamically scheduling multi-priority patients to a diagnostic facility in a public health care setting. Rather than maximizing revenue for the diagnostic facility, the challenge facing the resource manager is to allocate available capacity to incoming demand so that waiting time targets are achieved in a cost-effective manner. We will model the scheduling process as a Markov Decision Process. Since the state space is much too large for a direct solution, we solve the equivalent linear program through approximate dynamic programming. We present two theorems giving the optimal linear approximation for two potential cost structures of the scheduling process. Our results suggest an easily implementable booking policy that manages to maintain reasonable waiting times for a variety of demand streams.

This paper studies a class of Poisson mixture models that includes covariates in rates. This model contains Poisson regression and independent Poisson mixtures as special cases. Estimation methods based on the EM and quasi-Newton algorithms, properties of these estimates, a model selection procedure, residual analysis, and goodness-of-fit test are discussed. A Monte Carlo study investigates implementation and model choice issues. This methodology is used to analyze seizure frequency and Ames salmonella assay data.

No abstract