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Record W2330629943 · doi:10.3934/jimo.2007.3.29

Taking market forces into account in the design of production-distribution networks: A positioning by anticipation approach

2007· article· en· W2330629943 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

VenueJournal of Industrial and Management Optimization · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFacility Location and Emergency Management
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsVendorAnticipation (artificial intelligence)Production (economics)Computer scienceMonte Carlo methodSample (material)Operations researchMathematical optimizationFunction (biology)Distribution (mathematics)Industrial organizationMicroeconomicsEconometricsEconomicsBusinessMarketingMathematicsStatistics

Abstract

fetched live from OpenAlex

This paper presents an approach to take into account market opportunities when designing production-distribution networks. Three types of sub-markets found in several industrial contexts are analyzed: spot markets, contracts and Vendor Managed Inventory (VMI) agreements. For contracts and VMI agreements, customer preferences with respect to different logistics policies are considered. A price-supply function is proposed to model the spot market behavior. The production-distribution network design problem is formulated as a two-stage stochastic program with fixed recourse. Finally, a sample average approximation method (SAA), based on Monte Carlo sampling techniques, is used to solve the model.

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)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.039
GPT teacher head0.246
Teacher spread0.207 · 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