Introduction to the Analytic Hierarchy Process

· Springer
E-knjiga
83
str.

O ovoj e-knjizi

The Analytic Hierarchy Process (AHP) has been one of the foremost mathematical methods for decision making with multiple criteria and has been widely studied in the operations research literature as well as applied to solve countless real-world problems. This book is meant to introduce and strengthen the readers’ knowledge of the AHP, no matter how familiar they may be with the topic.

This book provides a concise, yet self-contained, introduction to the AHP that uses a novel and more pedagogical approach. It begins with an introduction to the principles of the AHP, covering the critical points of the method, as well as some of its applications. Next, the book explores further aspects of the method, including the derivation of the priority vector, the estimation of inconsistency, and the use of AHP for group decisions. Each of these is introduced by relaxing initial assumptions. Furthermore, this booklet covers extensions of AHP, which are typically neglected in elementary expositions of the methods. Such extensions concern different numerical representations of preferences and the interval and fuzzy representations of preferences to account for uncertainty. During the whole exposition, an eye is kept on the most recent developments of the method.

O autoru

Matteo Brunelli received his Bachelor and Master degrees from the University of Trento, Italy, and his Doctorate from Åbo Akademi University, Finland. In the past years he has been a visiting researcher at University of Turku (Finland), Auckland University of Technology (New Zealand), and University of California, Berkeley (United States). He is presently an Academy of Finland postdoctoral research at the Department of Mathematics and Systems Analysis, Aalto University, Finland.

He has worked on topics related to the Analytic Hierarchy Process for almost ten years and presently his research interests cover mathematical modelling of preference relations, representations of uncertainty, and aggregation functions.

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