Modeling reliability considerations in the design and analysis of cellular manufacturing systems.
Bibliographic record
Abstract
Reliability plays a vital role in the overall performance of cellular manufacturing systems (CMSs). Machine failures significantly impact the fulfillment of due dates and other performance criteria, despite the option of part rerouting to alternative workstations. These facts suggest a need for the consideration of machine reliability during the operation allocation process. Attempting to improve a system's reliability invariably results in higher costs. It follows that the ideal strategy for achieving optimum balance lies in an approach that integrates both cost and reliability information. A mixed integer multi-objective mathematical programming model that incorporates machine reliability and cost considerations is developed for the design of CMSs. The model selects processing route for each part type which maximizes the overall system reliability of machines along the route, while minimizing the overall costs. The proposed approach provides flexible routing, ensuring high CMS performance by minimizing the impact of machine failure through the provision of alternative process routes. To account for the constant and increasing failure pattern of manufacturing machines, the CMS design model considers both the exponential and Weibull distribution approaches. A performance evaluation criterion in terms of system availability for the part-process plan assignment based on the exponential distribution is also developed. Applicability of the model is demonstrated by solving example problems by following the ∈-constraint approach. Optimization techniques for solving such models for large practical-size problems require a substantial amount of time and memory space; therefore, a heuristic, based on the basic steps to simulated annealing and solution generation procedure of genetic algorithm is developed. The heuristic is evaluated by comparing the solutions generated by the heuristic with the LP relaxation solution for the large problems and optimal solution for the smaller-sized problems. The results reveal that the heuristic performs well in various problem instances for reliability and cost combinations. The sensitivity of the model outputs to key factors has also been investigated. A reliability-based, preventive maintenance (PM) planning model is also incorporated, allowing CMS to restrict deterioration of machines due to usage and age and improve system reliability. A procedure for the integration of PM planning into the CMS design model is included for overall reliability and cost improvement of the CMS. Example problems are solved to illustrate the model's applicability.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .D37. Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 4032. Thesis (Ph.D.)--University of Windsor (Canada), 2006.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".