An integrated control policy for cost and waste minimization in unreliable hybrid manufacturing-remanufacturing systems
Bibliographic record
Abstract
This paper addresses production‑planning and control in a mixed‑configuration hybrid manufacturing – remanufacturing system where one dedicated facility manufactures, while a second shared facility alternates – via setup operations – between manufacturing and remanufacturing modes. This configuration provides valuable flexibility and superior resource utilization but must still contend with capacity limits, stochastic demand and returns, machine failures, and setup‑induced downtime. The objective is to establish an integrated control policy that synchronizes manufacturing, remanufacturing, setup, and disposal through hedging‑point production rules and stock‑threshold triggers for setup and disposal. A multi‑objective simulation – optimization approach, combining response‑surface methodology with a desirability function, optimizes the policy parameters to minimize total cost and disposed returns. Sensitivity experiments confirm robustness under different managerial priorities; emphasizing remanufacturing reduces waste, whereas favoring manufacturing mitigates stockouts and holding costs. These guidelines enable decision‑makers to leverage the mixed configuration’s capabilities while maintaining a practical balance between cost efficiency and sustainability in failure‑prone environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".