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Record W2101137675 · doi:10.1109/hicss.2001.926326

Recent trends in logistics and the need for real-time decision tools in the trucking industry

2005· article· en· W2101137675 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsGlobal Positioning SystemElectronic data interchangeThe InternetDecision support systemComputer scienceOrder (exchange)Real-time dataInformation technologyOperations researchTransport engineeringTelecommunicationsBusinessEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

For the last ten years or so, the freight transportation industry has been facing new challenges, such as time-sensitive industrial and commercial practices as well as the globalization of markets. In response to these changes, new information-related technologies have developed rapidly: electronic data interchange (EDI) and the Internet, the Global Positioning System (GPS) via satellites, and decision support systems (DSSs). These technologies can greatly enhance the operations planning capability of freight carriers in as much as they make use of this information in order to optimise their operations. GPS, EDI and the Internet can also provide the necessary information required to achieve real-time computer-based decision making using appropriate operations research techniques. Today's decision support tools must therefore be designed to be used in a real-time environment. This paper describes this environment and proposes optimization tools that can be made available to motor carriers.

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)0.001
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 score0.486
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.000
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 head0.051
GPT teacher head0.323
Teacher spread0.273 · 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

Quick stats

Citations25
Published2005
Admission routes1
Has abstractyes

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