The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A cosine maximization method for the priority vector derivation in AHP
Open accessAbstract
The derivation of a priority vector from a pair-wise comparison matrix (PCM) is an important issue in the Analytic Hierarchy Process (AHP). The existing methods for the priority vector derivation from PCM include eigenvector method (EV), weighted least squares method (WLS), additive normalization method (AN), logarithmic least squares method (LLS), etc. The derived priority vector should be as similar to each column vector of the PCM as possible if a pair-wise comparison matrix (PCM) is not perfectly consistent. Therefore, a cosine maximization method (CM) based on similarity measure is proposed, which maximizes the sum of the cosine of the angle between the priority vector and each column vector of a PCM. An optimization model for the CM is proposed to derive the reliable priority vector. Using three numerical examples, the CM is compared with the other prioritization methods based on two performance evaluation criteria: Euclidean distance and minimum violation. The results show that the CM is flexible and efficient.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Analytic Hierarchy Process
SAATY, T. L., & KEARNS, K. P. (1985) · Analytical Planning · doi:10.1016/b978-0-08-032599-6.50008-8 · Cited by 15,357
A scaling method for priorities in hierarchical structures
Saaty, T. L. (1977) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(77)90033-5 · Cited by 10,360
Introduction to Modern Information Retrieval
Salton, G., & McGill, M. J. (1983) · Virtual Defense Library (Ministerio de Defensa) · Cited by 9,680
Introduction to modern information retrieval
Dillon, M. (1983) · Information Processing & Management · doi:10.1016/0306-4573(83)90062-6 · Cited by 5,909
Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process
Saaty, T. L. (2000) · Medical Entomology and Zoology · Cited by 2,635
The SMART Retrieval System—Experiments in Automatic Document Processing
Salton, G. (1971) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,252
Data mining introductory and advanced topics
Dunham, M. H. (2003) · Cited by 1,447
Marketing Applications of the Analytic Hierarchy Process
Wind, Y., & Saaty, T. L. (1980) · Management Science · doi:10.1287/mnsc.26.7.641 · Cited by 1,368
The Analytic Hierarchy Process: Structuring, Measurement, and Synthesis
Wang, J., Chakraborty, C., & Ouyang, H. (2011) · Encyclopedia of Decision Making and Decision Support Technologies · doi:10.4018/9781599048437.ch003 · Cited by 1,328
Multicriteria Methodology for Decision Aiding
French, S. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600981 · Cited by 1,280
Cited by
Si, S.-L., You, X.-Y., Liu, H.-C., & Zhang, P. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/3696457 · Cited by 1,082
EVALUATION OF CLASSIFICATION ALGORITHMS USING MCDM AND RANK CORRELATION
KOU, G., LU, Y., PENG, Y., & SHI, Y. (2012) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622012500095 · Cited by 566
Self-citationMulti-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach
Awasthi, A., Govindan, K., & Gold, S. (2018) · International Journal of Production Economics · doi:10.1016/j.ijpe.2017.10.013 · Cited by 544
Integrated Determination of Objective Criteria Weights in MCDM
Zavadskas, E. K., & Podvezko, V. (2016) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622016500036 · Cited by 365
Kou, G., Yang, P., Peng, Y., Xiao, F., Chen, Y., & Alsaadi, F. E. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105836 · Cited by 357
Self-citation
Metadata
- Authors
- Kou, G., & Lin, C.
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 235
- Issue
- 1
- Pages
- 225-232
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2051440091
- Cited by
- 347
- References
- 29
- FWCI
- 35.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A cosine maximization method for the priority vector derivation in AHP. https://app.cochat.ai/papers/10.1016/j.ejor.2013.10.019
- The paper
- Kou, G., & Lin, C. (2014). A cosine maximization method for the priority vector derivation in AHP. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2013.10.019
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
