Optimizing a Hybrid Warranty Policy with Remanufactured Parts and Service Enhancement
Bibliographic record
Abstract
Increasing value and complexity in warranty contract design has made policy optimization necessary for manufacturers. This paper investigates a one-dimensional, non-renewing hybrid warranty with “as-good-as-new” (AGAN) replacements. The warranty spans two periods. AGAN remanufactured second-hand parts are used as replacements in the first period, while minimal repairs are provided in the second period. However, customers may pay a premium to instead extend the AGAN replacements into the second period. A nonlinear optimization model is proposed for maximizing manufacturer profit based on the ratio between first and second warranty period length, the ratio between base unit price and enhanced warranty cost, and the age at which second-hand parts are salvaged rather than remanufactured. The proposed warranty is a novel extension in sustainable manufacturing and has its value demonstrated via numerical experiments while linking basic product aspects to warranty design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".