A New Approach for Reinforcement of Project DEMATEL-FMCDM-TODIM Fuzzy Approach

A New Approach for Reinforcement of Project DEMATEL-FMCDM-TODIM Fuzzy Approach

Rajshree Srivastava (Chandigarh University, India), Shiv Kumar Verma (Glocal University, India) and Vikas Thukral (Charles River Development, USA)
Copyright: © 2018 |Pages: 10
DOI: 10.4018/978-1-5225-6029-6.ch014
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Abstract

This chapter describes how an effective work towards software project risk plays a vital role in determining the accomplishment of any project. In this chapter, the aim is to associate fuzzy criteria decision-making based on the approaches for the development of an assessment framework. This framework will be helpful in terms of identification and ranking the software risk according to its characteristics which will be helpful in decision-making of a software lifecycle. For the assessment for the risk of a project, there is an integration of fuzzy decision-making trial, evaluation laboratory trial and fuzzy multi-criteria decision. This new method proposed will be effective in terms of ranking and as well as to measure the software risk factors.
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Fuzzy DEMATEL Method

Having an accurate value in a complex system is very difficult during the preceding of decision making. The main objective of using this method is to evaluate the interrelationship among numerous criteria, multiple attributes, handling uncertainty and having subjective ambiguity with the decision-making process.

DEMATEL approach had achieved success in various contexts namely in the cases of knowledge management strategies, global managers competencies, assessment of project outcome and in the planning of industrial (Kahneman et al., 1979). According to prior studies DEMATEL has been engaged with the subjective weights of each decision criteria. Hence for the computation of weights and ranking risk factors, fuzzy DEMATEL method is used (Wang et al., 2008).It involves some steps to compute weight and ranking risk factors these are setting up direct-relation matrix; defining the fuzzy linguistic variables; transformation of initial direct-relation matrix to TFNs; then obtaining the average value; having a direct-relation matrix; then setting up total-relation matrix; obtain the sum of columns and rows of each criterion and last obtaining the weight of the individual based on its weight.

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