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Record W7133061668

Defining, modeling, and solving a real university course timetabling problem

2007· dissertation· W7133061668 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTSpace · 2007
Typedissertation
Language
FieldDecision Sciences
TopicScheduling and Timetabling Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsContinuationQuality (philosophy)Benchmark (surveying)Course (navigation)Function (biology)Work (physics)Decomposition
DOInot available

Abstract

fetched live from OpenAlex

The central thesis of this dissertation is that the problem definition stage of solving real world problems should be directly studied and that problems must be studied in their entirety to create useful solutions. The problem definition stage has been identified as important yet is not directly studied in operations research (OR). This work is an introduction of such research to OR. Timetabling has received much attention from researchers and this work is a continuation of such effort. We conduct an analysis of the timetabling problem at the Faculty of Applied Science and Engineering at the University of Toronto (APSC), showing how difficult it would be to create a definition for a mathematical model. We also create evaluation criteria for APSC and show that quality measures in an objective function cannot accurately represent the desired metrics. Finally, we apply mathematical programming and decomposition techniques to some benchmark timetabling problems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.407
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it