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Record W2150318828 · doi:10.1287/trsc.37.1.23.12818

Outsourcing Logistics: Designing Transportation Contracts Between a Manufacturer and a Transporter

2003· article· en· W2150318828 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 · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiddingConsignmentOutsourcingOrder (exchange)Process (computing)Operations researchComputer scienceProduction (economics)Supply chainExploitBusinessEngineeringMicroeconomicsEconomicsMarketing

Abstract

fetched live from OpenAlex

In our environment, a manufacturer procures material from a supplier and the supplier brings it in bulk to a warehouse. This material is then consigned to the plant area, where it is utilized as an input of the production process. This consignment process is outsourced by the manufacturer and a transportation company is selected via a bidding mechanism. Primarily, we consider the problem of designing parameters of a given contract for the transportation activity. We define three subproblems within the contract design problem that interact with each other to a certain extent. These subproblems are the vehicle dispatching problem, inventory control problem, and contract value problem. We define these problems, exploit their interactions, and propose solution methods. Moreover, we present an approach to design such transportation contracts, which is based on solving these subproblems in an order for an adequate number of contract parameter combinations and selecting the one that minimizes expected total costs for the manufacturer.

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)0.000
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: none
Teacher disagreement score0.963
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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