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Record W2047904670 · doi:10.1080/07408170701744819

Heuristics for allocation of reconfigurable resources in a serial line with reliability considerations

2008· article· en· W2047904670 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIIE Transactions · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAdvanced Queuing Theory Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaNational Science CouncilDivision of Civil, Mechanical and Manufacturing InnovationEngineering Research CentersNational Science Foundation
KeywordsHeuristicsServerComputer scienceReliability (semiconductor)Monotone polygonThroughputLine (geometry)Distributed computingResource allocationHeuristicMathematical optimizationComputer networkOperating systemMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

We consider the allocation of reconfigurable resources in a serial line with machine failures. Each station is equipped with non-idling dedicated servers while the whole system is equipped with a finite number of reconfigurable servers. The reconfigurable servers are available to be assigned to any station and all servers are allowed to collaborate on a single job. We provide conditions for a policy to achieve throughput optimality. We also show in the two-station case that transition monotone optimal policies exist. We discuss heuristics based on the two-server model that reduce average holding costs significantly. These heuristics are compared to several heuristics from the literature via a detailed numerical study.

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 score0.492
Threshold uncertainty score0.337

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 head0.024
GPT teacher head0.235
Teacher spread0.211 · 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