A Combined Fuzzy Analytic Network Process and Fuzzy-TOPSIS Model for Project Risk Assessment

Author(s):  
Ebrahim Rezaee Nik ◽  
Seyed Hessameddin Zegordi ◽  
Ahad Nazari ◽  
Masatoshi Sakawa ◽  
Fereydoon Honari Choobar
2019 ◽  
Vol 25 (2) ◽  
pp. 168-183 ◽  
Author(s):  
Yan Li ◽  
Xinyu Wang

The public-private partnership (PPP) has been adopted globally to meet intensifying demands for public facilities and services. However, PPP projects contain a variety of risks which may lead to project failure. Many researchers have explored risk factors associated with PPP projects in developing countries. However, these investigations have limited their aim to understanding risk impact without considering the interactions of these factors. Hence, to fill this gap, this study proposes a risk assessment method, addressing vital interrelationships and interdependencies. Two methodologies, fuzzy analytic network process (F-ANP) and interpretive structural modeling (ISM), were applied to avoid vagueness and data inaccuracies. The primary contributions of this paper were considering the relationships among risk factors and risk priority; and offering a risk analysis approach based on linguistic scales and fuzzy numbers to reflect different neutral, optimistic and pessimistic viewpoints from expert respondents’ judgments. Results from this analysis showed that legal and policy risk was the most influential and interdependent risk, and interest rate risk was the most essential risk in Chinese PPP projects. The ISM structure diagram demonstrated that most of 35 identified risk factors had high driving and dependence power. This study proposed a systematic and practical method to identify and assess PPP risk factors, utilizing an integrated approach consisting of F-ANP and ISM, which has not been used for risk assessment in the construction field. This paper provides a new risk assessment tool and a basis for risk management strategies in the construction engineering and management field.


Author(s):  
Semih Önüt ◽  
Selin Soner Kara ◽  
Derya Tekin

In this chapter, a combined fuzzy multiple criteria decision making (MCDM) methodology for supporting the undesirable location selection process is presented. The undesirable location selection process is formulated by using the fuzzy analytic network process (FANP), one of MCDM methods, which is used to evaluate the most suitable alternatives of undesirable facility locations. Then, the fuzzy TOPSIS (technique for order performance by similarity to ideal solution) is used to rank competing locations in terms of overall performances. Since different alternatives and various quantitative, qualitative, tangible, and intangible criteria should be considered in the selection process, fuzzy MCDM methods have been found to be a useful approach to solve this kind of location selection problems including vagueness and imprecision in the human judgments.


2014 ◽  
Vol 13 (06) ◽  
pp. 1283-1323 ◽  
Author(s):  
Homa Samadi ◽  
Salman Nazari-Shirkouhi ◽  
Abbas Keramati

Due to ever-increasing trend in outsourcing information technology projects in today's competitive world, the risk management in information technology outsourcing (ITO) projects is a challenging issue. Hence, this paper reviews and extracts present corresponding risks by literature review to implement risk management in ITO. After reviewing a number of frameworks in the literatures related to prioritizing of extracted risk factors, a new framework is presented to determine the priority of them. Because of network structure of the proposed framework and multi-dimensional nature of the project risk, the fuzzy analytic network process (fuzzy ANP) is applied to prioritize risk factors. Also, since identifying and prioritizing of risk factors cannot necessarily meet the organization's needs related to the project risk, the ways to respond to these factors are evaluated. For this purpose, responses to the five highest ranked risk factors are considered. Prioritization of responses to these risk factors is done by applying fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) based on four criteria: quality, cost, time, and scope. Results, achieved from experts' judgment, show that the risk factor "Supplier's lack of expertise with an IT operation" is the most significant. Also, the best response for this factor, is "Review of monetary value and volume of suppliers' contracts prior to their selection" according to experts' point of view. In addition, a sensitivity analysis is carried out for validating the results.


2017 ◽  
Vol 50 ◽  
pp. 50
Author(s):  
Trần Thị Nhật Hồng ◽  
Trần Thị Mỹ Dung ◽  
Huỳnh Tấn Phong ◽  
Lê Thị Diễm Phương ◽  
Trương Hoàng Thơ

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Amir Farmahini Farahani ◽  
Kaveh Khalili-Damghani ◽  
Hosein Didehkhani ◽  
Amir Homayoun Sarfaraz ◽  
Mehdi Hajirezaie

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