CLUSTERS AND PIVOTS FOR EVALUATING A LARGE NUMBER OF ALTERNATIVES IN AHP

By Alessio Ishizaka. 2012

60 citations recorded by the source corpus.

AHP has been successful in many cases but it has a major limitation: a larger number of alter- natives requires a high number of judgements in the comparison matrices. In order to reduce this problem, we present a method with clusters and pivots. This method also helps with a further four problems of the Analytic Hierarchy Process. It enlarges the comparison scale, facilitates the construction of a consistent or near consistent matrix, eliminates the problem of the choice of the priorities derivation method and allows the use of incomparable alternatives.

Subfields: Control and Systems Engineering, Management of Technology and Innovation, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/d7d23b2dad69784cae5b9108299c53a161d2af96

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Salvatore Greco
2. Alessio Ishizaka
3. Gianpiero Torrisi
4. Menelaos Tasiou
5. Matteo Brunelli

In Management of Technology and Innovation

1. Alessio Ishizaka

In Management Science and Operations Research

1. Alessio Ishizaka
2. Salvatore Greco
3. Gianpiero Torrisi
4. Menelaos Tasiou
5. R. Słowiński
6. Marco Cinelli
7. Matteo Brunelli

Citations in the corpus

• On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness (2018, background): “That is, on the occasion that the number of indicators is very large, it exerts cognitive stress on decision makers, which in the AHP is amplified due to the pairwise comparisons required (Ishizaka 2012).”