FUZZY ANALYTIC HIERARCHY PROCESS: A COMPREHENSIVE LITERATURE REVIEW
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This literature review explores the advancements and applications of Fuzzy Analytic Hierarchy Process (FAHP) technique between 2019 and 2024, in addition to the studies that combined or compared FAHP with other methods. FAHP integrates Analytic Hierarchy Process (AHP) with fuzzy set theory to manage uncertainty and imprecision in Multi-Criteria Decision-Making (MCDM) problems. This review covers 85 papers from prominent journals using well-known databases. It introduces a novel taxonomy that categorizes FAHP research into three main categories: outcome types, methodological variations, and application domains, with further subcategories explained in this paper. This paper highlights diverse applications of FAHP across many fields and domains, proving FAHP’s effectiveness in addressing complex decision problems. Observations reveal FAHP’s strength in uncertain problems, while gaps in the literature call for further exploration in less applied fields like agriculture and healthcare. Other future research directions also are discussed in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| 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 it