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Record W2608166121

An Efficient Solution Approach for Combinatorial Bandwidth Packing Problem with Queuing Delays

2014· preprint· en· W2608166121 on OpenAlexaff
Sachin Jayaswal, Navneet Vidyarthi, Sagnik Das

Bibliographic record

VenueRePEc: Research Papers in Economics · 2014
Typepreprint
Languageen
FieldEngineering
TopicOptimization and Packing Problems
Canadian institutionsConcordia University
Fundersnot available
KeywordsBandwidth (computing)Integer programmingComputer scienceMathematical optimizationLinear programmingQueueing theoryBandwidth allocationCutting-plane methodTelecommunications networkRevenuePiecewise linear functionComputer networkMathematics
DOInot available

Abstract

fetched live from OpenAlex

The Combinatorial bandwidth packing problem (CBPP), arising in a telecommunication network with limited bandwidth, is defined as: given a set of request, each with its potential revenue and consisting of calls with their bandwidth requirements, deciding (i) a subset of the requests to accept/reject, and (ii) a route for each call in an accepted request, so as to maximize total revenue earned. However, telecommunication networks are generally characterized by variability in the call (bits) arrival rates and service times, resulting in delays in the network. In this paper, we present a non-linear integer programming model for CBPP accounting for such delays. By using simple transformation and piecewise outer-approximation, we linearize the model, and present an efficient cutting plane based approach to solve the resulting linear mixed integer program to optimality.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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
GenreMethods

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

Citations0
Published2014
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicOptimization and Packing ProblemsFrench-language works237,207