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Record W2170951393 · doi:10.1287/trsc.1120.0454

Designing Production-Inventory-Transportation Systems with Capacitated Cross-Docks

2013· article· en· W2170951393 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

VenueTransportation Science · 2013
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsColumn generationTruckMathematical optimizationInteger programmingCutting stock problemSafety stockSupply chainTransportation theoryFacility location problemComputer scienceOperations researchFixed chargeNonlinear programmingLinear programmingSet (abstract data type)Optimization problemNonlinear systemMathematicsEngineering

Abstract

fetched live from OpenAlex

We consider a two-echelon supply chain problem, where the demand of a set of retailers is satisfied from a set of suppliers and shipped through a set of capacitated cross-docks that are to be established. The objective is to determine the number and location of cross-docks and the assignment of retailers to suppliers via cross-docking so that the total cost of pipeline and retailers inventory, transportation, and facility location is minimized. We formulate the problem as a nonlinear mixed integer programming. We first derive several structural results for special cases of the problem. We also demonstrate that the Capacitated Plant Fixed-Charge Transport Location Problem is a special case of our problem. To solve the general problem, we show that it can be written as a cutting stock problem and develop a column generation algorithm to solve it. We investigate the efficiency of the proposed algorithm numerically. We then extend the problem by allowing different truck capacities as decision variables.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.019
GPT teacher head0.262
Teacher spread0.242 · 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