Lockout/Tagout and Operational Risks in the Production Control of a Transfer Line With Passive Redundancy
Bibliographic record
Abstract
This paper presents an analytical model for the joint determination of optimal production and occupational safety for a failure prone manufacturing system consisting of three machines (two machines as passive redundancy and a third machine in series with the previous ones) producing one type of part. These machines are subject to breakdowns and repairs and the control problem is subject to non-negative constraints on work-in-processes (WIP). The decision variables are the production rate of two main machines and a standby machine. The decision variables influence the WIP levels, the inventory levels and the system’s capacity. The system capacity is assumed to be described by a finite state Markov chain. The aim of this paper is to minimize the cost of WIP, inventory while respecting occupational safety. The proposed approach is based on the combination of analytical formalism, simulation modeling, design of experiments and response surface methodology to optimize a transfer line in passive redundancy producing one part type. The usefulness of the proposed approach is illustrated through a numerical example.
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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".