Modelling Congestion for Aggregate Production Planning in Open Queuing Networks
Bibliographic record
Abstract
The challenge in aggregate production planning for high-tech manufacturing industries such as aerospace, semiconductor manufacturing, or high precision components production is the variability in cycle times or cycle steps due to the rework required to meet very high levels (6-sigma) of quality.This variability at the lower planning level needs to be accounted for in aggregate planning level.Planning circularity, whereby cycle time depends on resource utilization while resource utilization is determined by cycle time continues to be an important problem in the aggregate planning literature.It is well known that ignoring congestion, as is the case in MRP-II based systems still widely in use, is inaccurate.In the presence of congestion, the relationship of WIP and throughput is nonlinear and bottleneck resources may shift constantly.The most common approach of addressing the nonlinear relationship between the WIP and the throughput is through the clearing function.Recent work by Omar et al. (2017) proposed a mixed-integer linear model for closed queuing production networks using fixed release planning.The challenge with this model is that it is difficult to scale up for typically sized problems scalability.This work extends the approach in Omar et al.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".