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Record W2128992727 · doi:10.5267/j.msl.2010.01.009

Optimum assembly line balancing by minimizing balancing loss and a range based measure for system loss

2010· article· en· W2128992727 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

VenueManagement Science Letters · 2010
Typearticle
Languageen
FieldEngineering
TopicAssembly Line Balancing Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMeasure (data warehouse)Computer scienceRange (aeronautics)Assembly lineOperations managementReliability engineeringEngineeringData mining

Abstract

fetched live from OpenAlex

An assembly line is a sequence of workstations, connected together by a material handling system, which is used to assemble components into a final product. The economic importance of assembly as a manufacturing process has led to extensive efforts for designing an assembly line to ensure efficiency and cost effectiveness of assembly operations. These efficiency and effectiveness can be maximized by minimizing balancing loss and system loss. The current work offers a range based measure for System loss and describes a technique for jointly minimizing balancing loss and system loss. To arrive at the optimum solution of the joint minimization problem, the same has been reduced to sequential marginal minimization problems and simulation technique has been installed for obtaining the optimal solution. For demonstration purpose, one example has been worked out to indicate the strength of the suggested method.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.615
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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