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An stochastic multiattribute acceptability analysis‐based method for the multiattribute project portfolio selection problem with rank‐level information
Author(s) -
Song Shiling,
Ang Sheng,
Yang Feng,
Xia Qiong
Publication year - 2019
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12447
Subject(s) - computer science , rank (graph theory) , selection (genetic algorithm) , preference , portfolio , mathematical optimization , operations research , ranking (information retrieval) , complete information , data mining , machine learning , mathematics , statistics , mathematical economics , combinatorics , financial economics , economics
In many cases of practical multiattribute project portfolio selection problems, it is hard to obtain accurate measurements of attributes and precise preference information. Even after a long and costly information gathering, the attribute measurements and the preference information can still be uncertain or inaccurate. Considerable cost saving will be obtained if the selection of an optimal project portfolio can be done using rank‐level information based on some or all the attributes, without knowing the preference information. In this paper, we propose a stochastic multiattribute acceptability analysis‐based method that can deal with mixed rank and cardinal attribute measurements and uses little or no weight information. In the proposed stochastic multiattribute acceptability analysis‐based method, the decision makers need not to express their preferences explicitly or implicitly, so it is particularly useful when no weight information is available at all. A numerical example involving selection of photovoltaic plants in an industrial province in Eastern China is provided to demonstrate the proposed method.