Optimal joint production, maintenance and product quality control policies for a continuously deteriorating manufacturing system
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
This article presents a study with the aim of optimizing a production system, consisting of a machine that produces a single type of parts, that degrades over time. This degradation affects the availability of the deployed machine and increases the defective rate of the manufactured products. The machine is subject to random failures and repairs. It has a defective rate which increases with its degradation. An overhaul of the machine allows to reduce this defective rate and bring it back to its initial condition. The objective of this study is to find a joint policy for production, maintenance and quality control, in order to increase the availability of the machine, improve the quality of the manufactured products and minimize the total cost of production. To achieve this goal, we formulated the research problem and used a stochastic dynamic programming approach to develop the Hamilton-Jacobi-Bellman (HJB) type optimum conditions. Then, we simulated a practical hybrid application to optimize the production of a Router class R CNC cutting machine that transforms polypropylene sheets in a Quebec (Canada) company specialized in the production of fire pumps. The obtained results allowed us to propose a critical threshold production policy, corrective and preventive maintenance strategies, and a sampling type of quality control to the company. Finally, we performed a sensitivity analysis to ensure the validation of the proposed policies.
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 | 0.002 | 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 | 0.001 |
| 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