1 / 26

Teacher Assignment Program

Teacher Assignment Program. Maria Lizarraga Project Defense Master of Computer Science Tuesday June 29, 2010 Department of Computer Science University of Colorado, Colorado Springs. Agenda. Introduction Background Information Solution Test Results Lessons Learned Future Research

lilli
Download Presentation

Teacher Assignment Program

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Teacher Assignment Program Maria Lizarraga Project Defense Master of Computer Science Tuesday June 29, 2010 Department of Computer Science University of Colorado, Colorado Springs Lizarraga

  2. Agenda • Introduction • Background Information • Solution • Test Results • Lessons Learned • Future Research • Summary Lizarraga

  3. Introduction • High School Timetabling Problem • Scheduling Problem Lizarraga

  4. Scheduling Models Lizarraga

  5. School Timetabling Problem Find: xijk (i = 1,….,m; j = 1,…,n; k= 1,…,p) Where: xijk = 0 or 1 (i = 1,…,m; j=1,…,n; k=1,…,p) Scheduling Component - Class Teacher Period Such that: p  xijk = rij (i = 1,…,n; j=1,…,n) k=1 n  xijk= 1 (i = 1,…,m; k=1,…,p) j=1 m  xijk= 1 (j = 1,…,n; k=1,…,p) i=1 Course Objective Function: m n p min  wijkxijk i = 1 j = 1 k = 1 Lizarraga

  6. Local Search Neighborhood Neighborhood Selection Lizarraga

  7. Genetic Algorithms Lizarraga

  8. Ant Colony Optimization Lizarraga

  9. Related Research • Michael Clark, Martin Henz, Bruce Love, QuikFix A Repair-based Timetable Solver, In Proceedings of the 7 th PATAT Conference, (2008) • Defu Zhang, Yongkai Liu, Stephen C.H. Leung, A simulated annealing with a new neighborhood based algorithm for high school timetabling problems, Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp 381-386 • Aldy Gunawan, K. M. Ng, H. L. Ong, A Genetic Algorithm for the Teacher Assignment Problem for a University in Indonesia, Information and Management Sciences, Vol. 19 No. 1. pp 1-16, 2008 • Djasli Djamarus, Ku Ruhana Ku-Mahamud, Heuristic Factors in Ant System Algorithm for Course Timetabling Problem, isda, pp.232-236, 2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009 Lizarraga

  10. Problem Definition • Machine Environment • Flexible Flow Shop • Constraints • Objective Function • total weight of all violated constraints Lizarraga

  11. Solution • Teacher Assignment Program Lizarraga

  12. Strategic Tabu Search Algorithm Lizarraga

  13. Select class to swap Switch teacher with classes within same period Switch with unassigned teachers within same period Neighborhood Selection Lizarraga

  14. Mutation Lizarraga

  15. Speed Test Lizarraga

  16. Random vs Strategic Lizarraga

  17. Tabu List Size Lizarraga

  18. Equalized Weights Random Neighborhood Selection Strategic Neighborhood Selection Lizarraga

  19. Lessons Learned • Application requirement versus constraint • Tailoring solution to problem • Remove concept of hard and soft constraints Lizarraga

  20. Further Research • Tabu list for each period • Dynamically changing the weights when stuck in a local minimum • Dynamically changing to Random Neighborhood Selection when stuck in local minimum • Investigate how to have multiple course sections Lizarraga

  21. Summary • Sensitive to the number of constraints • Strategic Neighborhood selection performs better than Random Neighborhood select, (speed and quality) • Size of Tabu List made little difference • Equalizing weights can help escape local minimum Lizarraga

  22. Questions Lizarraga

  23. Supplemental Slides Lizarraga

  24. Tabu Search Candidate Selection Lizarraga

  25. Simulated Annealing Candidate Selection • Acceptance Probability Function e (-/T) Where:  = selectedNeighbor.OFV – candidate.OFV T = current temperature • Cool Rate Tn = a * Tn-1 Lizarraga

  26. Neighborhood Examples Lizarraga

More Related