Combined Redundancy Allocation and Maintenance Planning Using a Two-Stage Stochastic Programming Model for Multiple Component Systems
Bibliographic record
Abstract
A new modeling approach is presented to optimally and simultaneously design the configuration of a multicomponent system and determine a maintenance plan with uncertain future stress exposure. Traditionally, analytical models for system design and maintenance planning are applied sequentially, but this new model provides an integrated approach to make decisions considering the lifecycle cost of the system. Specifically considering the influence of uncertain future usage stresses on component and system reliability, the integrated redundancy allocation and maintenance planning problem is formulated as a two-stage stochastic programming model with recourse. In this model, the system is exposed to uncertain usage scenarios with their associated probabilities of occurrence or likelihood. The decision variables for the first stage are the selection of component types and the number of components to be used in the system, and these variables are modeled before the uncertainty is revealed. The second-stage variables, involving a recourse function, are the preventive maintenance plan, which defines optimal maintenance times for planned replacement of components under distinct usage scenarios. Numerical examples and sensitivity analysis on series-parallel systems demonstrate applications of the proposed model and provide further insights. The comparisons of the proposed integrated approach to traditional sequential method show advantages of the proposed model in cost saving.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".