Date of Graduation

5-2020

Document Type

Thesis

Degree Name

Bachelor of Science in Industrial Engineering

Degree Level

Undergraduate

Department

Industrial Engineering

Advisor/Mentor

Sullivan, Kelly M.

Committee Member/Reader

Cassady, Richard

Abstract

University degree plans must be carefully planned so that they allow students the best chance of succeeding. Although for the better, with the advancement of technology and its incorporation into the classroom, it can be argued that the complexity and difficulty of some long-established engineering core classes has changed. With this trend certain combinations of engineering courses have become unfavorable in terms of course withdrawal and fail rates stemming from the interaction of course challenges. A wealth of data has been collected on this topic and will be utilized in this project. As one can imagine, the probability of success in a student completing a degree has infinitely many variables. One area this complex problem can be simplified is modeling the degree curriculum as an Activity-on-Node Network, a long-established fundamental engineering model. Courses are represented as nodes in the network, and the flow is restricted by precursors and successors, thus representing pre- and co-requisite relationships. With the knowledge of experimental failure probabilities in certain courses, individual student grade reports, and established pre-requisite constraints, this project aims to apply modern optimization tools to university curricula in hopes that the best solution can be obtained to maximize the probability of success.

Keywords

networks; graph theory; shortest path problem; optimization; network optimization

Download

Share

COinS