Production and setup policy optimization for hybrid manufacturing-remanufacturing systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hybrid systems that use both raw materials (manufacturing mode) and returned products (remanufacturing mode) as a supply for their production process are considered. The system studied consists of one facility that necessitates setup for switching from one production mode to another. As in industrial practice, the flow rate of returned products is usually below the market demand, switching from one mode to another is unavoidable for meeting the demand. Therefore, determining the optimal production and setup policies is critical for effectively planning production process and reducing the manufacturing cost. Evaluating system performance, we take into account production costs in manufacturing and remanufacturing modes, serviceable and return inventory costs, backlog and setup costs. We present first an analytical solution for optimal production and setup schedule along the production cycles, considering the case of reliable systems. These cycles are shown to contain intervals of production at maximal rates as well as on-demand manufacturing and on-return remanufacturing. Next, for failure-prone systems, the optimality conditions in the form of Hamilton–Jacobi–Bellman (HJB) equations are developed. Solving HJB equations numerically, the optimal production and setup policies are calculated, and it is demonstrated that the optimal trajectories converge to the production cycles) of the type determined analytically beforehand. The sensitivity analysis of the obtained solutions (both analytical and numerical) over system parameters is presented to validate the proposed approach and demonstrate the robustness of the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it