Integrated just-in-time production and imperfect maintenance management considering random quality degradation
Bibliographic record
Abstract
Modern manufacturing systems face challenges due to their unpredictable nature and limited output capacity. This paper proposes an integrated production and maintenance model to address these challenges, aiming to optimize system performance while minimizing costs. The primary aim is to develop a novel control policy that combines just-in-time (JIT) production strategies and imperfect maintenance policies. To achieve this, we develop a comprehensive model that incorporates stochastic processes, such as the Ornstein-Uhlenbeck process, to capture the random nature of defect generation. Differential equations are utilized to simulate material flow and logical processes within the production system. Through extensive numerical simulations and sensitivity analyses, we explore the influence of various cost parameters and stochastic process parameters on system behavior. Additionally, the sensitivity analysis of Ornstein-Uhlenbeck process parameters sheds light on their role in defect generation and system performance. Furthermore, the analysis highlights the economic advantages of the proposed control policy, emphasizing the importance of optimizing inventory levels. In conclusion, our study provides valuable insights into the design and optimization of integrated production and maintenance systems with stochastic dynamics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".