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Record W4401650586 · doi:10.1016/j.ifacol.2024.07.111

Optimizing a Capacitated Vehicle Routing Problem with Scheduled Arrival, Split Deliveries within Time Windows and Emission Consideration

2024· article· en· W4401650586 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

VenueIFAC-PapersOnLine · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsTransport Canada
Fundersnot available
KeywordsVehicle routing problemComputer scienceRouting (electronic design automation)Arrival timeOperations researchTransport engineeringComputer networkEngineering

Abstract

fetched live from OpenAlex

The Capacitated Vehicle Routing Problem (CVRP) has gained significant attention in both academic and industrial circles due to its pivotal role in optimizing logistic systems. In the context of evolving distributor companies and the growing integration of logistics with broader societal concerns such as climate considerations, this paper delves into a CVRP variant that includes time windows and split deliveries. Real-world assumptions are incorporated to enhance the practical applicability of the study. A mathematical model is proposed to minimize both economic costs and pollutant emissions. Given the unavailability of cost information for all possible routes, a cost function is estimated through multiple linear regression, considering both distance and time factors simultaneously, in order to associate to each link costs and emissions. To validate the effectiveness of the proposed model, a real-world case study involving an industrial distribution company is investigated. The results demonstrate a significant improvement compared to the company’s current operational procedures.

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 score0.221
Threshold uncertainty score0.844

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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