Optimizing production while reducing machinery lockout/tagout circumvention possibilities
Bibliographic record
Abstract
Purpose – The optimization of production imposes a review of facility maintenance policies. Accidents during maintenance activities are frequent, sometimes fatal and often associated with deficient or absent machinery lockout/tagout. Lockout/tagout is often circumvented in order to avoid what may be viewed as unnecessary delays and increased production costs. To reduce the dangers inherent in such practice, the purpose of this paper is to propose a production strategy that provides for machinery lockout/tagout while maximizing manufacturing system availability and minimizing costs. Design/methodology/approach – The joint optimization problem of production planning, maintenance and safety planning is formulated and studied using a stochastic optimal control methodology. Hamilton-Jacobi-Bellman equations are developed and studied numerically using the Kushner approach based on finite difference approximation and an iterative policy improvement technique. Findings – The analysis leads to a solution that suggests increasing the “comfortable” inventory level in order to provide the time required for lockout/tagout activities. It is also demonstrated that the optimization of lockout/tagout procedures is particularly important when the equipment is relatively new and the inventory level is minimal. Research limitations/implications – This paper demonstrates that it is possible to integrate production, maintenance and lockout/tagout procedures into production planning while keeping manufacturing system cost objectives attainable as well as ensuring worker safety. Originality/value – This integrated production and maintenance policy is unique and complements existing procedures by explicitly accounting for safety measures.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".