Graduation Year


Document Type




Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Michael Weng, Ph.D.

Committee Member

Alan Hevner, Ph.D.

Committee Member

Hao Zheng, Ph.D.

Committee Member

Grisselle Centeno, Ph.D.

Committee Member

Bo Zeng, Ph.D.


deteriorating inventory control, deterministic demand, periodic review, stochastic demand, base stock policy, (s, S) policy, service level


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.