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Record W2741268399 · doi:10.1109/icsssm.2017.7996166

Mobile Facility Routing Problem with Service-Time-related Demand

2017· article· en· W2741268399 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 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.

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 score0.318
Threshold uncertainty score0.550

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 head0.011
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

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

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