Execution of revised BMIM similarity coefficient for part family formation in reconfigurable manufacturing system
Bibliographic record
Abstract
The reconfigurable manufacturing system (RMS) is an advanced manufacturing strategy that enables precise adjustment of functionality and capacity to meet fluctuating demands economically. RMS focuses on part families, allowing configurations to be adapted for new part requirements. Optimizing flow line design to produce various parts involves minimizing reconfigurations and associated costs by enhancing operation sequence similarity. This article proposes a novel sequence optimization using the Longest Common Subsequence (LCS) method to reduce bypassing moves and machine idle times. The study introduces a similarity coefficient derived from LCS and employs average linkage hierarchical clustering to categorize parts in a case study. Unlike traditional methods, this approach considers material movements both before the initial machine and after the final processing station, addressing gaps in bypassing move calculations. The impact of different weighting scenarios for Type-II moves (ω) and machine idleness (β) on clustering was examined. For example, with Type-II move weights (ω) of {1.0, 0.6, 0.3, 0.0} and equal weightings for bypassing moves (α) and machine idleness (β) set at 0.5, a threshold value of 0.3 results in eight clusters, such as Cluster 1 {1, 11, 10, 12} and Cluster 3 {3, 5, 6, 4, 15, 9, 13, 14, 7, 8}. Lower threshold values lead to fewer clusters with larger sizes, indicating a more consolidated part family grouping. Various Type-II move weights and material handling scenarios demonstrate how different weighting configurations affect clustering and part family sizes. This approach enhances RMS efficiency by integrating comprehensive material handling considerations and optimizing clustering based on operational similarities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".