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Inventory Control and Demand Distribution Characterization

This thesis studies three problems related to inventory control.
The first problem is motivated by the need to eliminate the
bullwhip effect in a supply chain. An important source of this
effect is the inventory control policy, which is originally
designed to smooth production in response to demand variation
along the supply chain arising from the customers. To address this
issue, we propose an estimation method based on the control
variate technique. A byproduct of this approach is a stabilizing
inventory control policy. We evaluate the effectiveness of the
proposed method using the models from the literature.

Generally, the derivation of the inventory policies requires the
knowledge of the specific demand distribution. Unfortunately, in
several cases the demand is not observable in a direct way. The
second problem is motivated by a practical application where only
partial demand information is observable. Towards this end we
derive estimators of the first two moments of the (daily) demand
by means of the renewal theoretical concepts. We also propose a
regression-based approximation to improve the quality of the
estimators. A series of numerical studies are carried out to
evaluate the accuracy and precision of the estimators and to
investigate the impact of the estimation on the optimality of the
inventory policies.

The last part of this dissertation studies a periodic-review
inventory system with regular and emergency orders. Emergency
orders, characterized by shorter lead-time, higher ordering cost
and higher setup cost, are placed when the inventory level becomes
critically low. Based on our assumptions, we formulate a dynamic
programming model and prove the optimality of state-dependent sS
type polices for both emergency and regular orders. We also derive
analytic properties of the optimal policies. We gain some
managerial insights into the optimal policies and cost performance
from numerical studies.
Date14 February 2005
CreatorsBai, Liwei
PublisherGeorgia Institute of Technology
Source SetsGeorgia Tech Electronic Thesis and Dissertation Archive
Detected LanguageEnglish
Format669536 bytes, application/pdf

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