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Record W6991871335

Integer Programming Models for ï¬nding Optimal Part-Machine Families

2013· dissertation· en· W6991871335 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.

venuePublished in a venue whose home country is Canada.
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

VenueLibrary and Archives Canada (Government of Canada) · 2013
Typedissertation
Languageen
FieldSocial Sciences
TopicOnline Learning Methods and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsInteger programmingLinear programmingProcess (computing)Factory (object-oriented programming)Integer (computer science)Branch and priceBasis (linear algebra)
DOInot available

Abstract

fetched live from OpenAlex

In this thesis, we develop integer programming models which find the optimal part-machine family solutions, that disaggregate a factory process at the lowest cost. The groupings created using the methods presented in this thesis can then act as the basis for the application of Group Technology, which include machine placement, job scheduling, and part routing. Four exact 0−1 Linear Programming techniques are developed and presented. The first 0 − 1 Linear Programming technique only focuses on part subcontracting as a means to disaggregate, and the second only focuses on machine duplication to disaggregate. The final two methods both yield part-machine family disaggregation through simultaneous part subcontracting and machine duplication. Once these methods are applied to example problems, the results provide the exact solutions, which have not been found in previous work.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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