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Z-number based earned value management (ZEVM): A novel pragmatic contribution towards a possibilistic cost-duration assessment
مدیریت ارزش به دست آمده مبتنی بر عدد Z (ZEVM): سهم عملگرا جدید نسبت به ارزیابی هزینه تمام شده احتمالی-2020 The Earned value management (EVM) is one of the simplified analytical cost-duration assessment tools which
assist project managers in monitoring the status of the project undertaken. The EVM has been elaborated by both
deterministic and uncertain numbers such as fuzzy logic in the light of time. Even though cost-duration analysis
is so sensitive and fluctuating in projects, the adopted approaches were unable to consider the conspicuous
unreliability which is always involving the decision-making data. This problem impedes project managers to
trust the foreseen inferences. To help in overcoming this critical deficiency, Z-numbers were proposed to take
possibilities and reliabilities into account. Applying Z-numbers and possibilistic modeling in the EVM is a
challenging topic which causes the accuracy of cost-duration tracing results to be significantly enhanced. This
paper presents the application of z-numbers for modeling the earned value indicators and proves the superiority
of the ZEVM against traditional fuzzy EVM. This work originally adds to the state-of-the-art literature on earned
value management by presenting a proposal and applications of a new as Z-Earned Value Management (ZEVM).
An illustrative case is resolved to magnify the capability of the proposed framework in dealing with higher levels
of uncertainty associated with decision-making data. Keywords: Earned value management | Fuzzy sets | Project evaluation | Uncertainty | Z-number |
مقاله انگلیسی |
2 |
A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment
روشی برای افزایش قابلیت اطمینان برنامه های کاربردی سیستم خبره در ارزیابی ریسک احتمالی-2019 In highly complex industries, capturing and employing expert systems is significantly important to an organizations
success considering the advantages of knowledge-based systems. The two most important issues within
the expert system applications in risk and reliability analysis are the acquisition of domain experts professional
knowledge and the reasoning and representation of the knowledge that might be expressed. The first issue can be
correctly handled by employing a heterogeneous group of experts during the expert knowledge acquisition
processes. The members of an expert panel regularly represent different experiences and knowledge.
Subsequently, this diversity produces various sorts of information which may be known or unknown, accurate or
inaccurate, and complete or incomplete based on its cross-functional and multidisciplinary nature. The second
issue, as a promising tool for knowledge reasoning, still suffers from lack of deficiencies such as weight and
certainty factor, and are insufficient to accurately represent complex rule-based expert systems. The outputs in
current expert system applications in probabilistic risk assessment could not accurately represent the increasingly
complex knowledge-based systems. The reason is the lack of certainty and self-assurance of experts when
they are expressing their opinions. In this paper, a novel methodology is presented based on the concept of Znumbers
to overcome this issue. A case study in a high-tech process industry is provided in detail to demonstrate
the application and feasibility of the proposed methodology. Keywords: Confidence level | Z-numbers | Fault tree analysis | Spherical hydrocarbon storage tank |
مقاله انگلیسی |