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Record W2996899773 · doi:10.1016/j.ifacol.2019.11.127

Automotive Returnable Container Management with RFID:A Simulation Approach

2019· article· en· W2996899773 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIFAC-PapersOnLine · 2019
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutomotive industryContainer (type theory)Supply chainEconomic shortageComputer scienceDiscrete event simulationManufacturing engineeringSupply chain managementAutomotive engineeringBusinessEngineeringSimulationMechanical engineering

Abstract

fetched live from OpenAlex

Returnable containers are essential and critical for manufacturing operation and logistics in the automotive industry. However, container management is still affected by problems such as container shortage, losses or inefficient handling. These problems lead to a lack of accurate and timely data about container flow throughout the automotive supply chain. In this work, we study a RFID based automotive container supply network, and a discrete-event simulation (DES) approach is used to evaluate the impact of RFID on real case of an automotive returnable container supply chain. The simulation model and method provide the tool to analyze container supply chain performances. Computational results show that using RFID can bring relevant benefits to the supply chain.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.837

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 communication0.0000.000
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 head0.003
GPT teacher head0.184
Teacher spread0.181 · 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