Improvement of VIKOR Method With Application to Multi-Objective Design Problems
Bibliographic record
Abstract
For the design process of mechanical or electrical devices it is often necessary to consider multiple objectives. The design problem can then be formulated as multi-objective optimization problem. Multiple objectives can be conflicting and to pick a design solution a trade-off between those is required. A good trade-off is important for a successful product. Different decision making methods are available aiming towards a successful design trade-off; a commonly used method is the VIKOR method. This paper focuses on aspects of this method and reveals some weaknesses. However, a different normalization method is introduced that overcomes these aspects. Next, a minimum weight margin is established that gives information about the stability of a design solution picked by the VIKOR method. The weight margin is helpful for elucidating the decision maker’s uncertainty in the original weight assignment. The modified VIKOR method is then applied to the design of a wearable body sensor network and the design of an EEG electrode. The two design examples show the strength of the new modified VIKOR method resolving shortcomings by the original VIKOR method. Finally, the modified VIKOR method is compared with the theory of fuzzy measures and integrals as a multi-criteria decision making method.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".