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Record W173371885

Integrating product modularization and assembly line reconfiguration decisions: a genetic algorithm approach

2006· article· en· W173371885 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venueinternational conference on Modelling and simulation · 2006
Typearticle
Languageen
FieldEngineering
TopicAssembly Line Balancing Optimization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsControl reconfigurationModular programmingModular designComputer scienceGenetic algorithmProduct (mathematics)Mathematical optimizationProduct lineInteger (computer science)AlgorithmEngineeringMathematicsManufacturing engineeringEmbedded systemProgramming languageMachine learning
DOInot available

Abstract

fetched live from OpenAlex

To avoid conflicting decisions in modular product design and assembly system reconfiguration, the two issues have to be addressed jointly. In this paper, the integrated problem is formulated as a non-linear integer model. The quality loss function (QLF) is used to quantify non-comensurable and conflicting objectives. Several evaluation metrics in both design and manufacturing are incorporated into the model. The integrated nature of the problem leads to NP-hard complexity of the model. A genetic algorithm (GA) is therefore developed to provide near optimal solutions. The GA has been applied to a modular product.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.761

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.035
GPT teacher head0.259
Teacher spread0.224 · 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