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Record W3162788655

Application of revenue management in supply chain of postal services

2017· article· en· W3162788655 on OpenAlexaff
Ashkan Teymouri, Amir H. Khataie, Pavel A. Andreev, Craig Kuziemsky

Bibliographic record

VenueIEEE Conference Proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBusinessRevenueRevenue managementSupply chain managementProductivitySupply chainTruckOperations managementMarketingFinanceEngineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

E-commerce has been changing the rules of marketplace by empowered customers seeking immediate and flexible delivery. Advantages of online shopping opportunities lead to the constantly increasing parcel volumes that need to be shipped and delivered through postal services network. This evolution caused capacity management to become a serious challenge for postal organizations. Postal services have been upgrading their static value chain inherent to letter-mail to more dynamic for e-commerce parcel. The current solutions mainly focus on improving the productivity of collection and delivery (i.e. higher truck utilization) and increasing the efficiency of the equipment (i.e. fewer missorts). However, these solutions are temporary and expensive. This paper addresses the shortcoming of the existing solutions by conceptualizing application of revenue management and developing capacity management model for postal services.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.218
Teacher spread0.200 · 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 designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

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