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Optimal Discrete-in-Time Inventory Control of a Single Deteriorating Product with Partial Backlogging

The implicit assumption in conventional inventory models is that the stored
products maintain the same utility forever, i.e., they can be stored for an infinite period of
time without losing their value or characteristics. However, generally speaking, almost all
products experience some sort of deterioration over time. Some products have very small
deterioration rates, and henceforth the effect of such deterioration can be neglected.
Some products may be subject to significant rates of deterioration. Fruits, vegetables,
drugs, alcohol and radioactive materials are examples that can experience significant
deterioration during storage. Therefore the effect of deterioration must be explicitly taken
into account in developing inventory models for such products.
In most existing deteriorating inventory models, time is treated as a continuous
variable, which is not exactly the case in practice. In real-life problems time factor is
always measured on a discrete scale only, i.e. in terms of complete units of days, weeks,
etc. In this research, we present several discrete-in-time inventory models and identify
optimal ordering policies for a single deteriorating product by minimizing the expected
overall costs over the planning horizon. The various conditions have been considered, e.g.
periodic review, time-varying deterioration rate, waiting-time-dependent partial
backlogging, time-dependent demand, stochastic demand etc. The objective of our
research is two-fold: (a) To obtain optimal order quantity and useful insights for the
inventory control of a single deteriorating product over a discrete time horizon with
deterministic demand, variable deterioration rates and waiting-time-dependent partial
backlogging ratios; (b) To identify optimal ordering policy for a single deteriorating
product over a finite horizon with stochastic demand and partial backlogging. The
explicit ordering policy will be developed for some special cases.
Through computational experiments and sensitivity analysis, a thorough and
insightful understanding of deteriorating inventory management will be achieved.

Identiferoai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-4867
Date29 October 2010
CreatorsTan, Yang
PublisherScholar Commons
Source SetsUniversity of South Flordia
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceGraduate Theses and Dissertations
Rightsdefault

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