MétaCan
Menu
Back to cohort
Record W7132919224

Systems subject to repair and maintenance actions: Modeling and optimization

2008· dissertation· W7132919224 on OpenAlexfundno aff
Diederik Lugtigheid

Bibliographic record

VenueTSpace · 2008
Typedissertation
Language
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoOntario Centres of Excellence
KeywordsContext (archaeology)OutsourcingTime horizonDecision modelDecision support systemOptimal decisionDecision problemOptimization problem
DOInot available

Abstract

fetched live from OpenAlex

In maintenance and reliability, the use of systems that are repairable is growing every year, as consumers and manufacturers are gradually moving away from "throw-away" products for economical and environmental reasons. In contrast to non-repairable systems for which the only decision to be made is the "when to replace" decision, for repairable systems the decisions to be made are more complex. Not only needs the "when to replace" decision be addressed, but in addition also the "when to repair" and "what to repair" decisions. This makes the optimization of repairable systems complex. Furthermore, the formulation of repairable systems optimization problems is influenced by the business context that surrounds the system under consideration. Therefore, at least in theory, numerous repairable system optimization models can be formulated, where each is defined by the system itself and the business context it belongs to. The first model, called the General Repair Restriction Model (GRRM), addresses the question when to replace or repair a repairable system over a finite horizon when the number of repairs to which the system can be subjected to is restricted. Also, the system owner does not have information on the detailed repair activities that are being carried out, and/or cannot control the "what to repair" decision due to the outsourcing of system repairs to specialized repair centres. A dynamic programming approach will be used to derive the structure of the optimal policies. The second model, called the Repair and Maintenance Indicator Model (RMI), addresses the same question when the system owner does have access to detailed repair information and can control the "what to repair" decision. The RMI model is a new repairable system model, and is aimed to accommodate a greater variety of repairable systems. Besides the capability to address the "what to repair" question, the RMI model can also identify the most critical parts of a system, which may be of particular importance to OEMs (Original Equipment Manufacturers) when prioritizing design modifications aimed to improve system reliability. The general purpose of the RMI model (in contrast to models previously published in the literature) is to establish a clear decision rule in terms of the parts to be replaced in each repair, and therefore goes beyond the traditional "age-reduction" or "intensity-reduction" factors which have been frequently used to specify the "degree of repair" whenever the system is in the repair shop. This will be of particular benefit for tradepersons in the repair shop, as the RMI model will establish not only the "degree of repair" but also how to accomplish this "degree" in more practical terms. Besides the theoretical results, for both models several examples and case studies will be presented. The case studies are based on real problems commonly encountered in industry. The case studies show that both models are realistic, with considerable practical applications. In this thesis, two repairable system optimization models will be addressed. These two models are based on two commonly found business contexts in industry (in particular the mining industry), but have not been addressed so far in the maintenance and reliability literature.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.751
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceSame topicReliability and Maintenance OptimizationFrench-language works237,207