A Multivariate Solution of the Multivariate Ranking and Selection Problem

1980 ◽  
Author(s):  
Edward J. Dudewicz ◽  
Vidya S. Taneja
Keyword(s):  
Ranking And Selection ◽  
Selection Problem ◽  
Multivariate Ranking

scholarly journals Personnel Ranking and Selection Problem Solution by Application of KEMIRA Method

2015 ◽  
Vol 11 (1) ◽  
pp. 51 ◽  
Author(s):  
Natalja Kosareva ◽  
Edmundas Kazimieras Zavadskas ◽  
Aleksandras Krylovas ◽  
Stanislav Dadelo
Keyword(s):  
Ranking And Selection ◽  
Selection Problem ◽  
Problem Solution ◽  
Second Stage ◽  
Criteria Weights

In this study KEmeny Median Indicator Rank Accordance (KEMIRA) method is applied for solving personnel ranking and selection problem when there are two subgroups of evaluating criteria. Each stage of KEMIRA method illustrated with the examples. In the first stage Kemeny median method is applied to generalize experts’ opinions for setting criteria priorities. Medians were calculated for all experts opinions generalization and for experts majority opinions generalization. In the second stage criteria weights calculated and alternatives ranking accomplished simultaneously by Indicator Rank Accordance method. The obtained solutions compared with the results received in previous work of authors.

scholarly journals A New Approach to Estimate the Critical Constant of Selection Procedures

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
E. Jack Chen ◽  
Min Li
Keyword(s):  
Monte Carlo ◽  
Approximation Algorithm ◽  
Ranking And Selection ◽  
Monte Carlo Integration ◽  
Selection Problem ◽  
Selection Procedures ◽  
New Approach ◽  
Normal Distributions ◽  
Critical Constant

A solution to the ranking and selection problem of determining a subset of size containing at least of the best from normal distributions has been developed. The best distributions are those having, for example, (i) the smallest means, or (ii) the smallest variances. This paper reviews various applicable algorithms and supplies the operating constants needed to apply these solutions. The constants are computed using a histogram approximation algorithm and Monte Carlo integration.

A DOWNSIDE RISK APPROACH FOR THE PORTFOLIO SELECTION PROBLEM WITH FUZZY RETURNS

2004 ◽  
Vol 09 (01) ◽  
Author(s):  
Teresa León ◽  
Vicente Liern ◽  
Paulina Marco ◽  
Enriqueta Vercher ◽  
José Vicente Segura
Keyword(s):  
Portfolio Selection ◽  
Downside Risk ◽  
Selection Problem ◽  
Portfolio Selection Problem ◽  
Risk Approach

On Laplacian energy of picture fuzzy graphs in site selection problem

2021 ◽  
pp. 1-18
Author(s):  
Mahima Poonia ◽  
Rakesh Kumar Bajaj
Keyword(s):  
Decision Making ◽  
Site Selection ◽  
Upper Bounds ◽  
Selection Problem ◽  
Hydropower Plant ◽  
Fuzzy Graph ◽  
Fuzzy Information ◽  
Fuzzy Graphs ◽  
Laplacian Energy ◽  
Selection For

In the present work, the adjacency matrix, the energy and the Laplacian energy for a picture fuzzy graph/directed graph have been introduced along with their lower and the upper bounds. Further, in the selection problem of decision making, a methodology for the ranking of the available alternatives has been presented by utilizing the picture fuzzy graph and its energy/Laplacian energy. For the shake of demonstrating the implementation of the introduced methodology, the task of site selection for the hydropower plant has been carried out as an application. The originality of the introduced approach, comparative remarks, advantageous features and limitations have also been studied in contrast with intuitionistic fuzzy and Pythagorean fuzzy information.

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