Application of MADM methods as MOORA and WEDBA for ranking of FMS flexibility
Bibliographic record
Abstract
Flexibility has been cited as a key factor to enhance the performance of flexible manufacturing system (FMS). The main aim of this paper is to rank the flexibility of FMS. The ranking decisions are complex in the manufacturing field to analyze a number of alternatives based on a set of some attributes. In this research, two MADM methods i.e. MOORA (i.e. multi-objective optimization on the basis of ratio analysis) and weighted Euclidean distance based approach (WEDBA) are used for ranking of flexibility in FMS for new part development. MOORA approach can give decision with or without considering relative importance of attributes i.e. attribute weights. While in WEDBA, integrated attribute weights are used for evaluation which included the subjective and objective weights of attributes. Objective weights are calculated by entropy method and subjective weights are calculated by analytic hierarchy process. MOORA is applied in two ways i.e. ratio based and reference point analysis. Ranking of fifteen flexibility of FMS done on the basis fifteen variables which effect flexibility of FMS. The results of MOORA and WEDBA approach shows that product flexibility has the top most flexibility in fifteen flexibilities and programme flexibility has the least impact in fifteen flexibilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".