Spare parts inventory management for substitute consumer products: an adaptive robust optimization approach
Bibliographic record
Abstract
In this study, a multi-period spare parts inventory system providing spare parts for several consumer durable products in an assortment is investigated. An original equipment manufacturer (OEM) fulfills the after-sales service for the sold products with a repair-replacement policy. The products can substitute each other and use common and dedicated spare parts. The failure quantity of each product is uncertain and influenced by the on-market product quantity, which is governed by customer preferences and changes over the planning periods. To repair the failed products, spare parts inventory is used. The OEM aims to minimize the total inventory costs including spare parts purchase cost, holding cost, and product backorder cost. We formulate this problem as a multi-stage adaptive robust optimization model when the probability distributions of product failures are unknown. An improved partition-and-bound method is designed to solve the model. We demonstrate its relative computational advantage over the classical partition-and-bound method through numerical experiments on small- and medium-sized problem instances.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| 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".