An introduction to optimization through mathematical programming and stochastic modeling techniques. Course topics include linear programming, transportation and assignment algorithms, Markov Chain queuing and their application on problems in manufacturing, health care, financial systems, supply chain, and other engineering disciplines. Special attention is placed on sensitivity analysis and the need of optimization in decision-making. The course is delivered through lectures and a weekly laboratory where students learn to use state-of-the-art software packages for modeling large discrete optimization problems.

A supervised investigation within an industrial engineering area of student interest. Professional elective.

One semester of paid part-time work experience in the field of industrial engineering or sustainable engineering. See the graduate program coordinator or RIT’s Office of Cooperative Education for further details.

One semester of paid full-time work experience in the field of industrial engineering or sustainable engineering. See the graduate program coordinator or RIT’s Office of Cooperative Education for further details.

Forecasting Methods will provide the engineering student with the skills necessary to perform data driven time series analysis from an engineering applications perspective. A process driven approach will be used covering the entire forecasting process from data preparation and pre-processing techniques to model selection, performance evaluation, and monitoring. A special emphasis will be placed on performance evaluation and improvement of models used to predict RIT energy demand and peak load days. The course will cover topics in data cleansing, data transformation, trend and seasonality analysis, smoothing techniques, regression analysis for forecasting, seasonal and non-seasonal ARIMA models, dynamic regression, neural networks and advanced modeling techniques for multivariate time series analysis. Lectures and assignments will focus on predicting RIT energy demand considering circuits with 2MW solar fields or similar data sets.

In conference with a faculty adviser, an independent engineering project or research problem is selected. The work may be of a theoretical and/or computational nature. A state-of-the-art literature search in the area is normally expected. A formal written thesis and an oral defense with a faculty thesis committee are required. Submission of bound copies of the thesis to the library and to the department and preparation of a written paper in a short format suitable for submission for publication in a refereed journal are also required. Approval of department head and faculty adviser needed to enroll.

This course is used by students who plan to study a topic on an independent study basis. The student must obtain the permission of the appropriate faculty member before registering for the course. Students registering for more than four credit hours must obtain the approval of both the department head and the adviser.