Introduction:

Industrial Engineering is concerned with the design, installation, improvement, evaluation, and control of technical systems.  The objective of Industrial Engineering is to promote quality, efficiency, and productivity by optimizing resources while concurrently minimizing costs.  The tools of industrial engineering are very diverse ranging from rigorous mathematical models and design of experiments, to continuous process improvement and operations research.  Computer Simulation, neural networks, and expert systems are all tools aimed at understanding, evaluating and optimizing dynamic systems.  Select from the topics below for more detailed information.

Stochastic Modeling
Stochastic modeling normally require the use of simulation of a system that has random behavioral component. Simulation is the modeling or imitation of the operation of real systems or processes over time.  The simulated model can be used to study a variety of "what if" scenarios about the real system, as well as evaluate or identify existing or potential problem areas.


Neural Networks
Neural network analysis is an artificial intelligence (AI) approach to mathematical modeling.  Neural networks can be implemented for advanced control, data and sensor validation, pattern recognition, diagnosis and prediction, fault classification, and multivariable quality control applications. 


Expert System Design
Expert systems are a class of programs that emulate human expertise in well-defined problem domains.  Expert systems use knowledge-based rules, heuristics, and inference to provide solutions to problems through various techniques such as process automation, system diagnostics, and forecasting.