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Record W2117639993 · doi:10.1287/moor.25.1.141.15207

Optimal Preventive Replacement Under Minimal Repair and Random Repair Cost

2000· article· en· W2117639993 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

VenueMathematics of Operations Research · 2000
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOptimal stoppingMathematical optimizationMathematicsMaximizationLimit (mathematics)SemimartingaleUnit (ring theory)Utility maximization problemApplied mathematicsMathematical economicsUtility maximization

Abstract

fetched live from OpenAlex

A repair/replacement problem for a single unit system with random repair cost is considered. When the unit fails, the repair cost is observed and a decision is made whether to replace the unit or repair it. We assume that the repair is minimal, i.e., the unit is restored to its functioning condition just prior to failure, without changing its age. The unit can be preventively replaced at any time. The problem is formulated as a continuous time decision problem and reduced to an optimal stopping problem in discrete time by applying results from the thoery of jump processes. The existence of the optimal policy is proved and its structure is found using semimartingale decomposition and λ-maximization technique. It is shown that the optimal policy is an age replacement, repair-cost-limit policy, and the optimal preventive replacement time and the repair cost limits can be obtained by solving a system of ordinary differential equations with boundary conditions.

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)0.001
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 score0.167
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.031
GPT teacher head0.320
Teacher spread0.289 · 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