An optimal production/maintenance strategy under lease contract with warranty periods
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate the optimal production policy and maintenance strategy for leased equipment under a lease contract with warranty periods. In order to have steady revenue, the lessor (owner) of the equipment may provide guaranty periods to encourage the lessee to sign a lease contract with a longer lease period. Design/methodology/approach – Under this production/maintenance scheme, the mathematical model of the expected total cost is developed and the optimal production planning and the corresponding optimal maintenance policy are derived by choosing the optimal warranty periods for the lessee in order to minimize the total cost. Findings – The influence of the production rates variation in the equipment degradation is considered by an increased failure rate according to both time and production rates. The impact of warranty periods on optimal maintenance planning will be studied thereafter. Finally, numerical examples are given to illustrate the analytical study and the effects of the warranty periods variation during the lease periods on the maintenance policy and consequently on the total cost. Originality/value – The paper proposes a new idea of production and maintenance coupling in the leasing aspect. This study shows that it has a novelty and originality relative to this type of problem which considers and proposes a new maintenance strategy for leasing contract. This originality characterized by the influence of two factors on the equipment maintenance strategy. First factor is the influence of the production variation production rates on the machine degradation degree that is new in the literature charactering by analytical equation that shows the evolution of the machine failure rate according to its use (which is in our case the production rate for each period) respecting in the same time the continuity of the equipment reliability for a period to another.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".