MétaCan
Menu
Back to cohort
Record W2741268399 · doi:10.1109/icsssm.2017.7996166

Mobile Facility Routing Problem with Service-Time-related Demand

2017· article· en· W2741268399 on OpenAlexaff
Mingyao Qi, Chun Cheng, Xin Wang, Wenwei Rao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceMetaheuristicMathematical optimizationScheduling (production processes)Variable neighborhood searchRouting (electronic design automation)Service (business)Vehicle routing problemQuality of serviceVariable (mathematics)Integer programmingComputer networkMathematicsAlgorithm

Abstract

fetched live from OpenAlex

The mobile facility routing problem involves routing and scheduling a set of mobile facilities from a central depot to serve geographically dispersed customers. In this paper, service-time-related demand rate and fairness constraints are considered. The demand rate of a location is a piece-wise function, which decreases step by step over the service time from an initial value. To guarantee fairness, a minimum service time requirement at each location is imposed. We construct a mixed integer program to formulate this problem, and develop a variable neighborhood search metaheuristic to solve it. Computational results demonstrate that proposed algorithm can generate high-quality solutions in a reasonable time.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.243
Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicVehicle Routing Optimization MethodsFrench-language works237,207