EconPapers    
Economics at your fingertips  
 

Generalized analytic network process

Bin Zhu, Zeshui Xu, Ren Zhang and Mei Hong

European Journal of Operational Research, 2015, vol. 244, issue 1, 277-288

Abstract: The analytic network process (ANP) is a methodology for multi-criteria decision making used to derive priorities of the compared elements in a network hierarchy, where the dependences and feedback within and between the elements can be considered. However, the ANP is limited to the input preferences as crisp judgments, which is often unfavorable in practical applications. As an extension of the ANP, a generalized analytic network process (G-ANP) is developed to allow multiple forms of preferences, such as crisp (fuzzy) judgments, interval (interval fuzzy) judgments, hesitant (hesitant fuzzy) judgments and stochastic (stochastic fuzzy) judgments. In the G-ANP, a concept of complex comparison matrices (CCMs) is developed to collect decision makers’ preferences in the multiple forms. From a stochastic point of view, we develop an eigenvector method based stochastic preference method (EVM-SPM) to derive priorities from CCMs. The main steps of the G-ANP are summarized, and the implementation of the G-ANP in Matlab and Excel environments are given in detail, which is also a prototype for a decision support system. A real-life example of the piracy risk assessment to the energy channels of China is proposed to demonstrate the G-ANP.

Keywords: Decision support systems; Decision analysis; Distribution; Simulation (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221715000314
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:244:y:2015:i:1:p:277-288

DOI: 10.1016/j.ejor.2015.01.011

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:ejores:v:244:y:2015:i:1:p:277-288