R
Raimo P. Hämäläinen
Researcher at Aalto University
Publications - 254
Citations - 7804
Raimo P. Hämäläinen is an academic researcher from Aalto University. The author has contributed to research in topics: Decision analysis & Decision support system. The author has an hindex of 43, co-authored 254 publications receiving 7337 citations. Previous affiliations of Raimo P. Hämäläinen include Loughborough University & Helsinki University of Technology.
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Journal ArticleDOI
On the measurement of preferences in the analytic hierarchy process
Ahti Salo,Raimo P. Hämäläinen +1 more
TL;DR: In this article, the authors apply multiattribute value theory as a framework for examining the use of pairwise comparisons in the analytic hierarchy process (AHP) and propose new balanced scales to improve the sensitivity of the AHP ratio scales.
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On the convergence of multiattribute weighting methods
TL;DR: This is the first experiment where the subjects created the alternatives and attributes themselves and suggests that the resulting weights are different because the methods explicitly or implicitly lead the decision makers to choose their responses from a limited set of numbers.
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How to benefit from decision analysis in environmental life cycle assessment (LCA)
TL;DR: The integration of decision analysis and LCA could improve LCA as a tool for decision making and an LCA study on beverage packaging systems is used to illustrate the new approach.
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Preference programming through approximate ratio comparisons
Ahti Salo,Raimo P. Hämäläinen +1 more
TL;DR: In this article, the authors propose an interactive process of preference programming which allows the decision maker to enter ambiguous preference statements by indicating the relative importance of factors as intervals of values on a ratio scale, and synthesize the interval judgments into dominance relations on the alternatives by solving a series of linear programming problems.
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Web-Hipre: Global Decision Support By Value Tree And AHP Analysis
TL;DR: Web-HIPRE provides a common platform for individual and group decision making and the most common weighting methods including AHP, SMART, SWING, SMARTER and value functions are supported.