Deadlock resolution in flexible manufacturing systems: A Petri nets based approach.
Bibliographic record
Abstract
Flexible Manufacturing Systems (FMSs) are characterized by concurrency, resource sharing, routing flexibility, limited buffer sizes, and variety of lot sizes. The sharing of resources and the limitations on buffer sizes may lead to deadlock situations. One of the most challenging problems in FMSs design and operation is to assign the shared resources to jobs efficiently and without causing deadlocks. To date, little has been done to achieve deadlock-free scheduling in FMSs. In this research a new efficient scheduling algorithm for finding an optimal or near-optimal deadlock-free schedule was developed based on the depth-first and backtracking search technique. Two efficient truncation techniques and three heuristic functions were developed and tested using several randomly generated case studies. The performance of flexible manufacturing systems that exhibits deadlocks was analyzed under different levels of routing flexibility and other factors using Petri Nets. It was expected that routing flexibility would complicate the Petri Net model and create new deadlocks, which in turn could negatively affect the system performance. The results showed that increasing routing flexibility improves the system performance, measured by average flow time, in systems exhibiting deadlocks. A novel heuristic deadlock-free rescheduling algorithm based on Petri Nets was developed in order to deal with machine breakdowns in real-time. It guarantees a deadlock-free new schedule and relies on local rather than global rescheduling. The existence of alternative routes, availability of material handling facilities, and the limitations of buffer capacities were considered. In conclusion, the thesis introduces an integrated approach for production scheduling, control and performance evaluation of flexible manufacturing systems that exhibit deadlocks. The first part takes care of optimizing the performance of the manufacturing system, by generating optimal or near optimal schedules, and avoiding the deadlock situations in the same time. The second part could be used in answering the questions of the what-if analysis. Finally, the third part maintains the production control in real-time.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .E46. Source: Dissertation Abstracts International, Volume: 62-10, Section: B, page: 4716. Adviser: Hoda ElMaraghy. Thesis (Ph.D.)--University of Windsor (Canada), 2001.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".