College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, (b) Identify optimization strategies through simulation, and (c) Verify the applicability/practicality of the identified solutions. The simulation consists of two different focus levels. The first is a high-level Skeleton Model, which shows a birds-eye view of the campus and the movement of people/vehicles in a day. Second is a Deep Dive Model of the campus, which identifies more moving parts and student/worker interactions. Once the models were generated, they were analyzed, optimized, and shown to university stakeholders for feedback on the results. As an outcome, a more optimized campus model was obtained for ERAU-DB.
","abstract_html":"<p>College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, (b) Identify optimization strategies through simulation, and (c) Verify the applicability/practicality of the identified solutions. The simulation consists of two different focus levels. The first is a high-level Skeleton Model, which shows a birds-eye view of the campus and the movement of people/vehicles in a day. Second is a Deep Dive Model of the campus, which identifies more moving parts and student/worker interactions. Once the models were generated, they were analyzed, optimized, and shown to university stakeholders for feedback on the results. As an outcome, a more optimized campus model was obtained for ERAU-DB.</p>","abstract_has_math":false,"creators":["Chaback, Benjamin E."],"institution":null,"degree_name":"Master of Systems Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-04-01T07:00:00Z","date_published":"2024-04-01T07:00:00Z","updated_at":"2026-07-27T19:25:29Z","subjects":["Simulation","Optimization","Operations","Simio","Students","Campus","Modeling","Industrial Engineering","Operational Research","Other Operations Research, Systems Engineering and Industrial Engineering","Systems and Communications","Systems Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/791","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Chaback, Benjamin E."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Systems Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Simulation","Optimization","Operations","Simio","Students","Campus","Modeling","Industrial Engineering","Operational Research","Other Operations Research, Systems Engineering and Industrial Engineering","Systems and Communications","Systems Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/791"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, (b) Identify optimization strategies through simulation, and (c) Verify the applicability/practicality of the identified solutions. The simulation consists of two different focus levels. The first is a high-level Skeleton Model, which shows a birds-eye view of the campus and the movement of people/vehicles in a day. Second is a Deep Dive Model of the campus, which identifies more moving parts and student/worker interactions. Once the models were generated, they were analyzed, optimized, and shown to university stakeholders for feedback on the results. As an outcome, a more optimized campus model was obtained for ERAU-DB.
"]},{"key":"dc:title","label":"Title","values":["Optimization of Human Interactions in the College Campus Model Via Simio Integration"]}]}],"canonical_facts":{"dc:creator":["Chaback, Benjamin E."],"dc:description.abstract":["College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, (b) Identify optimization strategies through simulation, and (c) Verify the applicability/practicality of the identified solutions. The simulation consists of two different focus levels. The first is a high-level Skeleton Model, which shows a birds-eye view of the campus and the movement of people/vehicles in a day. Second is a Deep Dive Model of the campus, which identifies more moving parts and student/worker interactions. Once the models were generated, they were analyzed, optimized, and shown to university stakeholders for feedback on the results. As an outcome, a more optimized campus model was obtained for ERAU-DB.
"],"dc:identifier":["https://commons.erau.edu/edt/791"],"dc:subject":["Simulation","Optimization","Operations","Simio","Students","Campus","Modeling","Industrial Engineering","Operational Research","Other Operations Research, Systems Engineering and Industrial Engineering","Systems and Communications","Systems Engineering"],"dc:title":["Optimization of Human Interactions in the College Campus Model Via Simio Integration"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Systems Engineering"]},"updated_at":"2026-07-27T19:25:29Z"}