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Record W2122999407 · doi:10.1287/mnsc.1050.0392

Efficient Production-Distribution System Design

2005· article· en· W2122999407 on OpenAlexaff
Samir Elhedhli, Jean‐Louis Goffin

Bibliographic record

VenueManagement Science · 2005
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsMcGill UniversityUniversity of Waterloo
Fundersnot available
KeywordsColumn generationRelaxation (psychology)Mathematical optimizationContext (archaeology)Cutting-plane methodUpper and lower boundsBranch and boundPoint (geometry)Distribution (mathematics)Production (economics)Plane (geometry)HierarchyInterior point methodMathematicsLinear programming relaxationComputer scienceSupply chainApplied mathematicsGeometryMathematical analysisLinear programmingInteger programming

Abstract

fetched live from OpenAlex

The production-distribution system design is an integral part of the general supply chain design. This paper proposes a novel solution methodology for this problem that is based on Lagrangean relaxation, interior-point methods, and branch and bound. Unlike classical approaches, Lagrangean relaxation is applied in a two-level hierarchy, branch and bound is based on a Lagrangean lower bound and column generation (branch and price), while interior-point methods are used within a cutting-plane context (analytic centre cutting-plane method—ACCPM). Numerical results demonstrate that the two-level approach outperforms the classical approach and provides a very sharp lower bound that is the (proven) optimal in most cases.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations56
Published2005
Admission routes1
Has abstractyes

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