Inventory management in reverse logistics with imperfect production, learning, lost sales, subassemblies, and price/quality considerations
Bibliographic record
Abstract
Reverse Logistics is the flow and management of products, packaging, components, and information from the point of consumption (i.e., the market) to the point of origin (i.e., manufacturers and suppliers). It is a collection of practices similar to those of supply chain management, but in the opposite direction, from downstream to upstream. Reverse logistics is a valuable solution to the hazards jeopardizing the environment, and it involves activities such as reuse, repair, remanufacture, refurbish, reclaim and recycle.Reverse logistics became an established line of research, covering several areas, including inventory control; though, several research gaps still exist, such as: ignoring switching costs between production and remanufacturing processes and learning effects, the assumption that production and remanufacturing processes are of perfect quality, remanufactured products are assumed to be as-good-as new, the assumption that returned products are treated as whole products while ignoring disassembly, collection rate of used items is independent of price and quality, and the assumption that pure remanufacturing and production policies are optimal. These research gaps are addressed in mathematical models to bring reverse logistics optimization closer to reality. Deterministic and stochastic components are considered here with numerical examples and results discussed. The key conclusions are as follows:The inclusion of the first time interval where no remanufacturing/repair exists, results in preventing the overestimation of inventory holding costs in the repairable stock. Assuming production and remanufacturing processes to be perfect, or ignoring learning effects in these processes, might not capture the benefits that product recovery programs are supposed to bring. Although works in the literature assumed pure remanufacturing is mathematically attainable but not feasible, this study shows that the pure remanufacturing case is not valid mathematically, which proves it to be infeasible. It is favourable to compensate customers to settle for remanufactured products instead of new ones. Considering disassembly of returns in the modelling of reverse logistics is proven beneficial. Finally, mixed production and remanufacturing policies are optimal rather than pure ones; and the inclusion of price and quality to determine return and collection rates is crucial.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".