Efficient Academic Scheduling at the U.S. Naval Academy

Abstract

This research project examined academic scheduling problems at the U.S. Naval Academy. The focus was on devising methods to construct good final exam schedules and improve existing course schedules by facilitation course changes. The final exam scheduling problem is an example of an NP-hard problem. These difficult problems do not admit efficient deterministic solutions. Several heuristic methods to treat these problems were considered. An approach using genetic algorithms showed particular promise. Genetic algorithms involve mating parent schedules to form favorable offspring schedules and then subjecting these new schedules to local mutation. A computer program implementing these ideas was created and tested. Section changes at the Naval Academy had been done on an ad-hoc basis, but this project determined that it could be streamlined and improved by using a centralized barter system. The barter technique accepts input listing desired section changes and identifies multi-student section changes to accommodate their desires. A prototype computer program that uses network flow algorithms to find such section changes was devised. In addition, a method incorporating integer programming techniques was examined and tested.

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Document Details

Document Type
Technical Report
Publication Date
May 02, 2003
Accession Number
ADA416203

Entities

People

  • David L. Zane

Organizations

  • United States Naval Academy

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Computer Programs
  • Computers
  • Electrical Engineering
  • Engineering
  • Genetic Algorithms
  • Heuristic Methods
  • Integer Programming
  • Linear Programming
  • Mathematics
  • Navigation
  • Operations Research
  • Political Science
  • Scheduling (Production)
  • Students
  • United States Naval Academy

Fields of Study

  • Computer science

Readers

  • Defense Technology Research and Development.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Biotechnology