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Record W1602650693 · doi:10.18757/ejtir.2012.12.3.2965

The Influence of Time Windows on the Costs of Urban Freight Distribution Services in City Logistics Applications

2012· article· en· W1602650693 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEuropean journal of transport and infrastructure research · 2012
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsTransport engineeringContext (archaeology)Service (business)Performance indicatorComputer scienceLevel of serviceQuality (philosophy)Urban areaOperations researchBusinessEngineering

Abstract

fetched live from OpenAlex

In freight distribution services a required quality level may have a relevant effect on transportation costs. For this reason an evaluation tool is useful to compare different service settings and support the decision, on the base of quantitative indicators. This paper proposes a method for cost evaluation in this context and presents an application to a case study concerning a freight distribution service, which operates on a wide road network having a city centre, a peripheral urban area and a peri-urban rural zone. A simulation method is proposed to obtain real-life scenarios in order to test the method and its indicators. The performance of each indicator has been evaluated in an experimental context to produce realistic test cases, using a trip planning tool and a demand generator. First, the behaviour of the indicators is analysed with regard to the time windows width planned for the service. Then, their ability in estimating the total transportation cost to satisfy all the requests, under different time period profiles, is shown. The results confirm the ability of the set of indicators to predict with a good approximation the transportation costs and therefore to be used in supporting the service quality planning decisions.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.240
Teacher spread0.218 · 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