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Record W4403280268 · doi:10.1016/j.ifacol.2024.09.074

Combination Warranty Optimization Model Using Reconditioned Parts Under Age Uncertainty

2024· article· en· W4403280268 on OpenAlex
Shlok Mulye, Abdelhakim Khatab, Claver Diallo, Uday Venkatadri

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

VenueIFAC-PapersOnLine · 2024
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWarrantyReliability engineeringComputer scienceEconometricsEngineeringEconomicsPolitical science

Abstract

fetched live from OpenAlex

Remanufacturing processes such as refurbishing and reconditioning of used products is one pillar of sustainable manufacturing as, not only, it provides financial opportunities, but also allows manufacturers to engage in sustainable practices. The remanufactured products are typically used as spare parts in maintenance or as replacement products to honor warranty contracts. The present paper develops a mathematical optimization model to determine the optimal combination warranty policy when remanufactured products with uncertain age are used for warranty replacements after being upgraded by the original equipment manufacturer. Numerical experiments are conducted to illustrate the validity of the proposed 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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.591
Threshold uncertainty score0.817

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 head0.022
GPT teacher head0.247
Teacher spread0.225 · 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