A series—parallel redundant reliability system for cellular manufacturing design
Bibliographic record
Abstract
This paper presents a novel, multi-objective mixed-integer programming model for designing a cellular manufacturing system (CMS) that minimizes the total cost and maximizes the overall system reliability. In general, it is impossible to avoid production interruptions while handling machine breakdowns. In this situation, changing the process route dynamically can provide a quick response to meet production requirements. By assuming alternative process plans for operation—part requirements, the concept of the ‘reliable route’ proposed in the literature is extended. In a redundant reliability system with a series—parallel configuration, each reliable route is associated with an operation of a part (i.e. an operation—part) as a parallel subsystem. This route consists of a number of units or alternative machines allocated to cells in such a way that parts are processed with the maximum reliability for a given period of time. When an alternative machine breaks down, unprocessed parts are transferred to the next predetermined machine on the reliable route in order to complete their processes. While the reliable route approach increases the overall system reliability, the operational costs of the system also increase. To assess the present proposed model as a useful decision tool for the manager, various numerical examples are solved and analysed. Finally, the related computational results are reported.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".