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Record W2010740862 · doi:10.5539/jms.v2n2p219

A Heuristic Based Approach to Solve a Capacitated Location-routing Problem

2012· article· en· W2010740862 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 Management and Sustainability · 2012
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMathematical optimizationSimulated annealingComputer scienceHeuristicVehicle routing problemGreedy algorithmSet (abstract data type)Routing (electronic design automation)AlgorithmMathematics

Abstract

fetched live from OpenAlex

In this paper we define a new approach to solve the location routing problem (LRP). LRP is a relatively new research direction within location analysis that takes into account vehicle routing aspects. In this research it is proposed a new heuristic method base on simulated annealing to solve a capacitated location routing problem. At first an initial solution is produced by a greedy approach and then encoding this initial solution to use in optimization language and finally apply neighborhood search method to improve the initial solution. Finally, to verify the quality of our new approach in term of solution quality, one well-known data problem set is selected. The numerical examples show the efficiency of our approach.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.251
Teacher spread0.237 · 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