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Record W4298006032 · doi:10.1155/2022/8253175

Metaheuristics for a Large-Scale Vehicle Routing Problem of Same-Day Delivery in E-Commerce Logistics System

2022· article· en· W4298006032 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.

venuePublished in a venue whose home country is Canada.
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

VenueJournal of Advanced Transportation · 2022
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsnot available
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsVehicle routing problemMetaheuristicComputer scienceVariable neighborhood searchRouting (electronic design automation)Mathematical optimizationScale (ratio)City logisticsAlgorithmOperations researchMathematicsTransport engineeringEngineeringComputer network

Abstract

fetched live from OpenAlex

In this paper, we introduce a new variant of large-scale vehicle routing problem that arises in the goods distribution of city e-commerce logistics, the multi-depot vehicle routing problem with order split and allocation (MD-CVRP-OSA), which incorporates the issue of split and allocation of online orders into traditional VRP. A mathematical formulation is constructed for the MD-CVRP-OSA, and an efficient metaheuristics algorithm based on a variable neighborhood search (VNS) solution framework is designed to solve it. The proposed method is tested on a large family of instances, including real-world data collected from JD.com, and the effectiveness of the VNS algorithm and also the algorithm components are analyzed.

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)0.001
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: none
Teacher disagreement score0.411
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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