scholarly journals THE LEGACY OF THE ANALYTIC HIERARCHY/NETWORK PROCESS

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Luis G Vargas
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Cognitive Processes ◽  
Brain Activity ◽  
Fundamental Equation ◽  
Pairwise Comparisons ◽  
Analytic Hierarchy ◽  
Network Process ◽  
Hierarchy Process ◽  
Process Group

There is no doubt that the Analytic Hierarchy Process is in itself a remarkable contribution to the field of decision making.  In this paper, I have tried to point out what I consider to be Tom Saaty’s three most important contributions within his Analytic Hierarchy Process: group decision making, conflict resolution and the fundamental equation of pairwise comparisons.  The third contribution occupied him most of his life in an attempt to connect pairwise comparisons with brain activity related to cognitive processes.  https://doi.org/10.13033/ijahp.v9i3.541

scholarly journals REFLECTIONS: MATHEMATICAL PRINCIPLES OF DECISION MAKING

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Birsen Karpak
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Sensitivity Analysis ◽  
Analytic Hierarchy Process ◽  
Analytic Network Process ◽  
Time Dependent ◽  
Pairwise Comparisons ◽  
Analytic Hierarchy ◽  
Network Process ◽  
Hierarchy Process

This article discusses my reflections on Mathematical Principles of Decision Making by Thomas Saaty  (Saaty T. L., 2010). In this book, Saaty very clearly explains his Analytic Hierarchy Process (AHP) theory for measuring both tangible and intangible factors. Experts judgments are elicited about the dominance of a factor over another one via pairwise comparisons using an absolute scale and priorities of the factors are derived. The important concepts of the AHP such as compatibility index, validation, sensitivity analysis for testing the robustness of the priorities derived, and its generalization to structures with dependence and feedback, and the Analytic Network Process (ANP) are given. Extensions of the theory to complex decisions involving benefits, opportunities, costs and risks and applications to resource allocation and conflict resolution are included, as well as the generalization to continuous and time dependent judgments is also covered.https://doi.org/10.13033/ijahp.v9i3.521  

The Analytic Network Process – Dependence and Feedback in Decision-Making

2011 ◽  
pp. 360-387 ◽  
Author(s):  
Thomas L. Saaty
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Market Share ◽  
Analytic Network Process ◽  
Analytic Hierarchy ◽  
Fundamental Scale ◽  
Network Process ◽  
Independence Assumptions ◽  
Hierarchy Process ◽  
Pair Wise Comparison

Simple multi-criteria decisions are made by deriving priorities of importance for the criteria in terms of a goal and of the alternatives in terms of the criteria. Often one also considers benefits, opportunities, costs and risks and their synthesis in an overall outcome. The Analytic Hierarchy Process (AHP) with its independence assumptions, and its generalization to dependence among and within the clusters of a decision — the Analytic Network Process (ANP), are theories of prioritization and decision-making. Here we show how to derive priorities from pair-wise comparison judgments, give the fundamental scale for representing the judgments numerically and by way of validation illustrate its use with examples and then apply it to make a simple hierarchic decision in two ways: pair-wise comparisons of the alternatives and rating the alternatives with respect to an ideal. Network decisions are discussed and illustrated with market share examples. A mathematical appendix is also included.

scholarly journals FAHPSort: A Fuzzy Extension of the AHPSort Method

2018 ◽  
Vol 17 (04) ◽  
pp. 1119-1145 ◽  
Author(s):  
Jana Krejčí ◽  
Alessio Ishizaka
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Pairwise Comparisons ◽  
Multi Criteria Decision Making ◽  
Powerful Method ◽  
Linguistic Terms ◽  
Ahp Method ◽  
Analytic Hierarchy ◽  
Decision Making Problem ◽  
Hierarchy Process

Analytic Hierarchy process (AHP) is a powerful method belonging to the full aggregation family of multi-criteria decision-making methods based on pairwise comparisons of objects. Since the information about the problem is usually not complete in real decision-making problems, it is difficult to express precisely the preferences on pairs of compared objects. This problem has been handled in the literature by introducing fuzziness into AHP. However, neither AHP nor its fuzzy extensions can deal with sorting decision-making problems, which form a significant part of decision-making problems. This paper presents the FAHPSort method — a fuzzy extension of the AHPSort method, which is an adaptation of the AHP method for sorting decision-making problems. The FAHPSort method handles the vagueness in the meaning of linguistic terms expressing the intensity of preference of one object over another one. Key properties of the FAHPSort method are described in the paper, and the method is illustrated in a decision-making problem.

Comparison among Original AHP, Ideal AHP and Moderate AHP Models

2016 ◽  
Vol 2 (5) ◽  
pp. 40 ◽  
Author(s):  
G. Marimuthu ◽  
G. Ramesh
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Decision Model ◽  
Pairwise Comparisons ◽  
Multi Criteria Decision Making ◽  
Analytic Hierarchy ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

Decisions always involve the getting the best solution, selecting the suitable experiments, most appropriate judgments, taking the quality results etc., using some techniques.  Every decision making can be considered as the choice from the set of alternatives based on a set of criteria.  The analytic hierarchy process (AHP) is a multi-criteria decision making and is dealing with decision making problems through pairwise comparisons.  This paper is concerned with the moderate AHP decision model is always same as the original AHP decision model.  It does not violate the rule itself.

THE PRIMITIVE COGNITIVE NETWORK PROCESS: COMPARISONS WITH THE ANALYTIC HIERARCHY PROCESS

2011 ◽  
Vol 10 (04) ◽  
pp. 659-680 ◽  
Author(s):  
KEVIN KAM FUNG YUEN
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Multicriteria Decision Making ◽  
Decision Processes ◽  
Cognitive Network ◽  
Analytic Hierarchy ◽  
Comparison Results ◽  
Network Process ◽  
Hierarchy Process ◽  
Better Than

This research proposes primitive cognitive network process (P-CNP), which comprises five decision processes, as an alternative of analytic hierarchy process (AHP). Two published cases using AHP are revised by P-CNP, and the validity and applicability of P-CNP are demonstrated. The comparison results indicate that AHP produces questionable results due to the ill-defined axioms of the perception of the paired difference, and suggest that the proposed P-CNP performs better than AHP in multicriteria decision-making problems.

On Moderate Fuzzy Analytic Hierarchy Process Pairwise Comparison Model With Subcriteria

2016 ◽  
Vol 2 (3) ◽  
pp. 54
Author(s):  
G. Marimuthu ◽  
G. Ramesh
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Fuzzy Ahp ◽  
Pairwise Comparison ◽  
Decision Models ◽  
Fuzzy Analytic Hierarchy Process ◽  
Analysis Method ◽  
Analytic Hierarchy ◽  
Comparison Model ◽  
Hierarchy Process

Decisions usually involve the getting the best solution, selecting the suitable experiments, most appropriate judgments, taking the quality results etc., using some techniques.  Every decision making can be considered as the choice from the set of alternatives based on a set of criteria.  The fuzzy analytic hierarchy process is a multi-criteria decision making and is dealing with decision making problems through pairwise comparisons mode [10].  The weight vectors from this comparison model are obtained by using extent analysis method.  This paper concern with an alternate method of finding the weight vectors from the original fuzzy AHP decision model (moderate fuzzy AHP model), that has the same rank as obtained in original fuzzy AHP and ideal fuzzy AHP decision models.

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