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Record W2081660128 · doi:10.1080/00207540701223394

Level schedules for mixed-model JIT production lines: characteristics of the largest instances that can be solved optimally

2007· article· en· W2081660128 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Production Research · 2007
Typearticle
Languageen
FieldEngineering
TopicAssembly Line Balancing Optimization
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMathematical optimizationScheduleScheduling (production processes)Shortest path problemProduction scheduleComputer scienceProduction (economics)Path (computing)Longest path problemCritical path methodMathematicsEngineeringComputer network

Abstract

fetched live from OpenAlex

Takt time and cycle time are design variables in JIT production. Actual production is a performance variable. The level production scheduling problem constructs a schedule that matches actual production to takt time and cycle time. The problem can be solved optimally by constructing a network of nodes and arcs in which each path through the network corresponds to a production schedule and the shortest path corresponds to the optimal level production schedule. When a problem instance is large the number of nodes and arcs is very large. It is critically important to (i) eliminate from the network nodes and arcs that cannot be on the shortest path and (ii) evaluate the remaining nodes and arcs efficiently. This paper examines the best algorithm for finding an optimal schedule and analyses, by solving previously unsolved instances from the literature, characteristics of the largest instances that can be solved optimally.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 science0.0010.000
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 head0.119
GPT teacher head0.372
Teacher spread0.253 · 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