Analytic Network Process (ANP) Method: A Comprehensive Review of Applications, Advantages, and Limitations
Bibliographic record
Abstract
Nowadays, multi-criteria decision-making (MCDM) methods possess manifold applications in many areas from engineering to supply chain and management. The analytic network process (ANP) method is one of the most widely used MCDM methods. ANP is an extended version of the analytic hierarchy process that enables feedback and interactions between and within clusters, making it a more comprehensive decision-making tool. This paper provides a detailed review of the ANP method, including its concept, process steps, application areas, advantages, and limitations. ANP has been applied to a wide range of decision-making problems, including project management, risk assessment, supplier selection, and product design. ANP's main advantages include its ability to handle complex decision-making problems with multiple criteria, subjective inputs, and interdependent relationships among criteria. This paper aims to provide a comprehensive understanding of the ANP method to help researchers and practitioners make more informed decisions when using this technique. Received: 22 March 2023 | Revised: 4 May 2023 | Accepted: 16 May 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".