In this thesis, we study the statistical issues in lost sales inventory systems, focusing on the complexity
arising from the stochastic demand. We model the demand by the Zero Inflated Poisson (ZIP) distribution.
The maximum likelihood estimator of the ZIP parameters taking censoring into account are derived
separately for the newsvendor and the (s, S) inventory systems. We also investigate the effect of the
estimation errors on the optimal policies and their costs. We observe from a simulation study that the MLE
taking censoring into account performed the best in terms of cost as well as policy among various estimates.
We then proceed to develop a Bayesian dynamic updating scheme of the ZIP parameters. It is applied
to the newsvendor system. We perform a simulation study to investigate the advantage of the Bayesian
updating approach over the traditional MLE approach. We conclude that the Bayesian pproach offers
a better learning technique when one lacks of good understanding of the demand pattern in the first few
periods. Since inventory policy affects the information acquisition and-the demand distribution updating process,
how to determine the optimal inventory policy when the demand distribution is yet to be learned is the
focus of the latter part of the thesis. We investigate the effect of demand censoring on the optimal policy in
newsvendor inventory models with general parametric demand distribution and unknown parameter values.
We provide theoretical proof of the conjecture that it is better off to adopt a higher than the myopic optimal
policy in the initial periods when demand is learned in a censoring system. We show that the newsvendor
problem with observable lost sales reduces to a sequence of single-period problems while the newsvendor
problem with unobservable lost sales requires a dynamic analysis. We explore the economic rationality for
this observation and illustrate it with numerical examples. / Business, Sauder School of / Graduate
| Identifer | oai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/12944 |
| Date | 05 1900 |
| Creators | Ding, Xiaomei |
| Source Sets | University of British Columbia |
| Language | English |
| Detected Language | English |
| Type | Text, Thesis/Dissertation |
| Format | 9286362 bytes, application/pdf |
| Rights | For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. |
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