An integrated ACO-AHP approach for resource management optimization
Bibliographic record
Abstract
The most often used operator to aggregate criteria in decision making problems is the classical weighted sum model or weighted sum model. However, in many problems, the criteria considered interact and a substitute to the weighted sum model has to be adopted. Multi-criteria decision making (MCDM) problems involve the ranking of a finite set of alternatives in terms of a finite number of decision criteria. Usually such criteria may be in conflict with each other. A typical problem in MCDA is concerned with the task of ranking a finite number of decision alternatives, each of which is explicitly described in terms of different characteristics often called decision criteria or objectives. This research applies an integrated multi-criteria decision making approach to design an optimal UAV resource management. In this approach, the ant colony optimization (ACO) is used firstly to obtain optimal solutions satisfying some path planning criteria, then, fuzzy analytic hierarchy process (AHP) is formulated to select the best set of UAVs. Due to vagueness and uncertainty, fuzzy set theory based AHP is employed in the decision making judgments, because it can handle uncertainty easily. The proposed method can be extended to any sensor network resource management problem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".