A prioritization model for locating relief logistic centers using analytic hierarchy process with interval comparison matrix

2015 ◽  
Vol 86 ◽  
pp. 173-181 ◽  
Author(s):  
Ali Bozorgi-Amiri ◽  
Saman Asvadi
Keyword(s):  
Analytic Hierarchy Process ◽  
Analytic Hierarchy ◽  
Comparison Matrix ◽  
Hierarchy Process ◽  
Interval Comparison

scholarly journals Two Approximation Models of Fuzzy Weight Vector from a Comparison Matrix

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Tomoe Entani
Keyword(s):  
Analytic Hierarchy Process ◽  
Weight Vector ◽  
Analytic Hierarchy ◽  
Lower Approximation ◽  
Comparison Matrix ◽  
Fuzzy Weight ◽  
Interval Weight ◽  
Hierarchy Process ◽  
Interval Comparison ◽  
Approximation Models

In this study, our uncertain judgment on multiple items is denoted as a fuzzy weight vector. Its membership function is estimated from more than one interval weight vector. The interval weight vector is obtained from a crisp/interval comparison matrix by Interval Analytic Hierarchy Process (AHP). We redefine it as a closure of the crisp weight vectors which approximate the comparison matrix. The intuitively given comparison matrix is often imperfect so that there could be various approaches to approximate it. We propose two of them: upper and lower approximation models. The former is based on weight possibility and the weight vector with it includes the comparison matrix. The latter is based on comparison possibility and the comparison matrix with it includes the weight vector.

scholarly journals Analytic Hierarchy Process by Least Square Method Revisit

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Scott Shu-Cheng Lin
Keyword(s):  
Analytic Hierarchy Process ◽  
Least Square Method ◽  
Least Square ◽  
Analytic Hierarchy ◽  
Comparison Matrix ◽  
Hierarchy Process

We study the paper of Saaty and Vargas to discuss the solutions for a comparison matrix derived by eigenvector method, least square method, and logarithmic least square method, respectively. We prove that the prediction of Saaty and Vargas is valid. Our result will provide a patch work for the theoretic foundation for Analytic Hierarchy Process.

Electronic Market Credit Risk Evaluation for Agricultural Products Based on Analytic Hierarchy Process

2011 ◽  
Vol 99-100 ◽  
pp. 852-856
Author(s):  
You Zhu Li ◽  
De Hua He
Keyword(s):  
Analytic Hierarchy Process ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Pairwise Comparison ◽  
Agricultural Products ◽  
Electronic Market ◽  
Analytic Hierarchy ◽  
Pairwise Comparison Matrix ◽  
Comparison Matrix ◽  
Hierarchy Process

In the study, electronic market credit risk evaluation for agricultural products based on analytic hierarchy process is proposed.Firstly, the evaluation indexes are analyzed and the hierarchic tree is formulated based on the evaluation indexes.Then, pairwise comparison matrix is established,and the consistency of discriminant matrix is judged.When the consistency of discriminant matrix is satisfied,the weight vector of the indexes which are used to establish the pairwise comparison matrix are obtained. And weight of each index is obtained.Finally,final decision making is obtained. The experimental results show that the evaluation of electronic market credit risk evaluation for agricultural products based on analytic hierarchy process is effective.

scholarly journals PRIORITIZATION OF ALTERNATIVES BASED ON ANALYTIC HIERARCHY PROCESS USING INTERVAL TYPE-2 FUZZY SETS AND PROBABILITY-THEORETICAL INTERVAL COMPARISON

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Konstantin Yury Degtiarev ◽  
Mikhail Yury Borisov
Keyword(s):  
Analytic Hierarchy Process ◽  
Decision Makers ◽  
Analytic Hierarchy ◽  
Domain Experts ◽  
Expert Assessments ◽  
Interval Type ◽  
Hierarchy Process ◽  
Interval Comparison ◽  
Theoretical Procedure

The Analytic Hierarchy Process (AHP) enables decision-makers to prioritize alternatives. However, when an expert expresses judgments using natural language statements (e.g. words or phrases) inherent vagueness of language constructs can cause the interpretation to be imprecise. The fuzzy Analytic Hierarchy Process (FAHP) can be viewed in the context of the classical AHP expansion. While performing pairwise comparisons domain experts are accustomed to operating with verbal terms in their judgments. Most existing FAHP approaches do not consider a human’s confidence in the estimates provided. This paper presents a model that gives weight to the constraints on domains of expert assessments as they are almost always supplied with certain degrees of confidence. Interval type-2 membership functions (IT2MF) along with the probability-theoretical procedure for comparison of intervals can be applied here as suitable modeling options. Empirical comparison of FAHP that makes use of triangular fuzzy numbers and IT2MF-based FAHP is also presented.   

scholarly journals APPLYING THE ANALYTIC HIERARCHY PROCESS TO OIL SANDS ENVIRONMENTAL COMPLIANCE RISK MANAGEMENT

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Izak Johannes Roux ◽  
Dr. Christos Makrigeorgis
Keyword(s):  
Analytic Hierarchy Process ◽  
Oil Sands ◽  
Pairwise Comparison ◽  
Environmental Compliance ◽  
Analytic Hierarchy ◽  
Pairwise Comparison Matrix ◽  
Comparison Matrix ◽  
Risk Criteria ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

<p>In 2013, oil companies in Alberta, Canada invested $32 billion in new oil-sands projects.  Despite the size of this investment, there is a demonstrable deficiency in the uniformity and understanding of environmental legislation requirements that translate into increased project compliance risks. In this paper, we applied the Analytic Hierarchy Process (AHP) to develop a priority list of environmental regulatory compliance risk criteria for oil-sands projects.  AHP belongs to the family of multicriteria decision-making (MCDM) techniques that utilizes a pairwise comparison matrix solicited from subject matter experts (SMEs) in the field as input.  The overall methodology itself consisted of 4 phases: (1) identification of the initial list of N potential environmental compliance risk criteria and verification of these criteria via a pilot survey; (2) formation of a pairwise comparison survey in the form of an N(N-1)/2 comparison matrix based on the verified criteria; (3) administration of the pairwise comparison matrix to a sample of 16 industry-specific SME’s; and (4) the application of the AHP method using SuperDecisions as a tool on the collected sample to rank the identified risk criteria. Our demonstrated results can potentially inform Alberta oil sands industry leaders about the ranking and utility of specific compliance risks as understood by experts and enable a more focused environmental compliance action to help increase legislative and public trust.</p>

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