Open Access. Powered by Scholars. Published by Universities.®
Operations Research, Systems Engineering and Industrial Engineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Regression Analysis Of Pacing When Running A Marathon, Hawkin Starke
Regression Analysis Of Pacing When Running A Marathon, Hawkin Starke
Industrial Engineering Undergraduate Honors Theses
Regression analysis can be an effective way of examining performance in the marathon event. By splitting up the race into segments or in runner terminology “splits” the significance of each segment as it relates to the total finish time can be explored. Because the idea of splits is already ingrained into the minds of runners, it makes intuitive sense to use these as the metrics to define a race. Additionally, marathons generally make participant age and gender date publicly available which can then be used to find trends within specific demographics. This tailors trends to smaller groups of people, making …
Economic Model Predictive Control Design Via Nonlinear Model Identification, Laura Giuliani, Helen Durand
Economic Model Predictive Control Design Via Nonlinear Model Identification, Laura Giuliani, Helen Durand
Chemical Engineering and Materials Science Faculty Research Publications
Increasing pushes toward next-generation/smart manufacturing motivate the development of economic model predictive control (EMPC) designs which can be practically deployed. For EMPC, the constraints, objective function, and accuracy of the state predictions would benefit from process models that describe the process physics. However, obtaining first- principles models of chemical process systems can be time-consuming or challenging such that it is preferable to develop physics-based process models automatically from process operating data. In this work, we take initial steps in this direction by suggesting that because experiments that are used to characterize first-principles models often target specific types of data, an …
Data-Based Nonlinear Model Identification In Economic Model Predictive Control, Laura Giuliani, Helen Durand
Data-Based Nonlinear Model Identification In Economic Model Predictive Control, Laura Giuliani, Helen Durand
Chemical Engineering and Materials Science Faculty Research Publications
Many chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details …
Emergency Diesel-Electric Generator Set Maintenance And Test Periodicity, Stephen John Fehr
Emergency Diesel-Electric Generator Set Maintenance And Test Periodicity, Stephen John Fehr
Engineering Management & Systems Engineering Theses & Dissertations
Manufacturer and industry recommendations vary considerably for maintenance and tests of emergency diesel-electric generator sets in emergency standby duty. There is little consistency among generator sets of similar technology, and manufacturers and their representatives often provide contradictory guidance. As a result, periodicity of emergency diesel-electric generator set maintenance and tests varies considerably in practice. Utilizing the framework proposed and tested by Fehr (2014), this research developed a parametric regression survival model of the reliability of modern diesel-electric generator sets in emergency standby duty as a function of maintenance, age, and cumulative run hours. A survival regression technique leveraging Cox’s (1972) …
Forecasting Us Army Enlistment Contract Production In Complex Geographical Marketing Areas, Joshua L. Mcdonald, Edward D. White, Raymond R. Hill, Christian Pardo
Forecasting Us Army Enlistment Contract Production In Complex Geographical Marketing Areas, Joshua L. Mcdonald, Edward D. White, Raymond R. Hill, Christian Pardo
Faculty Publications
Purpose: The purpose of this paper is to demonstrate an improved method for forecasting the US Army recruiting. Design/methodology/approach: Time series methods, regression modeling, principle components and marketing research are included in this paper. Findings: This paper found the unique ability of multiple statistical methods applied to a forecasting context to consider the effects of inputs that are controlled to some degree by a decision maker. Research limitations/implications: This work will successfully inform the US Army recruiting leadership on how this improved methodology will improve their recruitment process.
Practical implications: Improved US Army analytical technique for forecasting recruiting goals.
Aircraft Demand Forecasting, Kayla M. Monahan
Aircraft Demand Forecasting, Kayla M. Monahan
Masters Theses
This thesis aims to forecast aircraft demand in the aerospace and defense industry, specifically aircraft orders and deliveries. Orders are often placed by airline companies with aircraft manufacturers, and then suddenly canceled due to changes in plans. Therefore, at some point during the three-year lead time, the number of orders placed and realized deliveries may be quite different. As a result, orders and deliveries are very difficult to predict and are influenced by many different factors. Among these factors are past trends, macroeconomic indicators as well as aircraft sales measures. These predictor variables were analyzed thoroughly, then used with time …