A conditional randomized algorithms for university timetabling of teachers / Jezreel K. Pabia
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TextLanguage: English Publication details: 2001Description: 51 leavesSubject(s): Dissertation note: Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2001 Abstract: This study aimed to come up with a heuristic algorithm for university course timetabling, which will produce conflict-free while providing alternatives for teachers and a control on the variance of teaching loads. Teachers are selected at random, but they are first required to meet certain conditions before their actual assignment. The algorithm seeks a feasible solution given the maximum allowed load per teacher and allows the user to evaluate the produced solution given the variance of the loads of the teachers. Using concepts from probabilities and limits, bounds were set so that the algorithm would not continue to search in vain for long periods of time. Constraints were also included to avoid specific unwanted results such as cycling. The implementation of the algorithm into a computer program using Turbo C is a secondary objective for this study.
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Thesis
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University Library Theses | Room-Use Only | LG993.5 2001 A64 P33 (Browse shelf(Opens below)) | Not For Loan | 3UPML00010953 | |
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Thesis
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University Library Archives and Records | Preservation Copy | LG993.5 2001 A64 P33 (Browse shelf(Opens below)) | Not For Loan | 3UPML00020904 |
Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2001
This study aimed to come up with a heuristic algorithm for university course timetabling, which will produce conflict-free while providing alternatives for teachers and a control on the variance of teaching loads. Teachers are selected at random, but they are first required to meet certain conditions before their actual assignment. The algorithm seeks a feasible solution given the maximum allowed load per teacher and allows the user to evaluate the produced solution given the variance of the loads of the teachers. Using concepts from probabilities and limits, bounds were set so that the algorithm would not continue to search in vain for long periods of time. Constraints were also included to avoid specific unwanted results such as cycling. The implementation of the algorithm into a computer program using Turbo C is a secondary objective for this study.
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