Fuzziness and randomness in investment project risk appraisal

Rębiasz, Bogdan

doi:10.1016/j.cor.2005.05.006

Cited by 46 publications

(18 citation statements)

References 24 publications

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“…To do good risk mitigation therefore, analyst need both. One of the most intricate parts accompanying risk mitigation is the quantification of risk (Rebiasz, 2007). Some of these quantitative techniques are less applicable as they necessitate the need for detailed information which is generally not available at the planning stage and thus there is a difficulty in making accurate decisions (Dey, 2010).…”

Section: Resultsmentioning

confidence: 99%

A Review on Risk Mitigation of IT Governance

Pa¹,

Anthony²,

Nor³

et al. 2014

Information Technology J.

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A B S T R A C T Risk Mitigation (RM) is one of the important activities in risk management of IT governance. In IT governance project, IT risk mitigation emphasizes taking action early in a project to prevent the occurrence of undesired events or to reduce the consequences of their occurrence. The essential of RM in IT governance enables enterprise achieving "the new business changes, reduces IT project risks and future investment in IT governance projects. To get clear understanding in regards risk mitigation based on IT governance context, many relevant studies have been reviewed from different issues and aspects. The purpose of this study is to investigate related RM frameworks, models, processes, stakeholder involves and other issues based on a Systematic Literature Review (SLR) approach. This study presents the results of the systematic literature reviews on an evident of the RM in IT governance and it issues that need to be catered in the future. The finding of this review indicates that RM requires appropriate consideration to systematically reviewed on it numerous limitations, issues and current implementation.

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Section: Resultsmentioning

confidence: 99%

A Review on Risk Mitigation of IT Governance

Pa¹,

Anthony²,

Nor³

et al. 2014

Information Technology J.

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“…One of the branches on this process flow is developed similar to Rebiasz [32] to find and analyzing the project risk by determining fuzzy values for the project parameters and using in a simulation model by converting them into probability distribution. Different from Rebiasz [32], not only the fuzzy parameter values are converted into probabilistic values and analyzed via simulation model but also this study handles the fuzzy NPV by utilizing the fuzzy operations and interpreting directly the fuzzy project risk. Besides to that, calculation procedure and simulation analysis are also presented for directly stochastic and deterministic conditions.…”

Section: Simulation and Fuzzy Sets For Determination Of The Project Rmentioning

confidence: 99%

“…Among the studies contributing this trend; Iwamura [19] and Huang [20] with the constraint programming approach based on fuzzy data to optimize capital budget; using neural network as a simulation metamodel in economic analysis of risky projects in [21]; R&D project evaluation model based on fuzzy set priority in [22]; transportation projects selection process using fuzzy sets theory in [23]; risk management of an agricultural investment utilizing simulation in [24]; fuzzy capital budgeting in [25]; modeling project investment decisions using possibility theory in [26]; the capital budgeting through random numbers and fuzzy numbers in [27]; using simulation software to solve engineering economy problems in [28]; characterizing the optimal profit of an investment problem with trapezoidal fuzzy numbers in [29]; the evaluation of investment projects optimizing multiple criteria via simulation and response surface methodology in [10]; Multicriteria decision making in a fuzzy setting in [30]; the optimal investment project selection based on fuzzy numbers and genetic algorithm in [31]; project risk assessment by converting the fuzzy numbers to the near probability distributions and analyzing through simulation techniques in [32] can be examined in the literature.…”

Section: Simulation and Fuzzy Sets For Determination Of The Project Rmentioning

confidence: 99%

An integrated decision support approach for project investors in risky and uncertain environments

Armaneri

Özdağoğlu

Yalçınkaya

2010

Journal of Computational and Applied Mathematics

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a b s t r a c tIn project investment decisions, it is often assumed that estimated values of project parameters are certain and they would not deviate by the time. However, project parameters normally change during a life cycle of the project. Therefore, an existence of a deviation or gap between forecasted values and actual values is inevitable. Because of the uncertainty of the future, forecasting the true and exact values of project parameters is almost impossible. In this study, an integrated decision support approach based on simulation and fuzzy set theory is proposed for project investors in risky and uncertain environments. This approach determines the risk levels of the projects and helps investors to make investment decisions. In the scope of the study, a flowchart is presented to guide to decision maker in different situations of information uncertainty that belongs to project parameter values. Via this flowchart, the values of project parameters can be chosen depending on how they are determined (deterministic, stochastic or fuzzy) by project analyst. Besides, calculating and analyzing the project risk in all possible situations would be easier. Illustrative examples are given to demonstrate the application of this approach.

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“…Since the model was made for investment portfolios, it was not suitable for project selection problems. Rebiasz [52] developed a model to assess projects risk with fuzzy or random parameters. This simulation-based model was only aimed at project risk evaluation and was not a project portfolio selection model.…”

Section: Introductionmentioning

confidence: 99%

R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach

Mohagheghi

Mousavi

Vahdani

et al. 2016

Neural Comput & Applic

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In today's ever-changing and highly uncertain environment, organizations depend on research and development (R&D) activities to adapt intensive growth of technology. One of the most important and trickiest tasks of any developing firms is to define new projects. This process gets difficult when it comes to choosing an appropriate portfolio from a set of candidate projects. Since organizations are faced with limited resources of R&D and budget constraints, they have to choose a project portfolio that mitigates the corresponding risk and enhances the overall value of portfolio. Therefore, the purpose of this study is to introduce a practical model to select the best and the most proper project portfolio while considering project investment capital, return rate, and risk. The ever-changing and highly uncertain environment of projects is addressed by utilizing interval type-2 fuzzy sets (IT2FSs). In this paper, a new model of R&D project evaluation is first introduced. This model includes a new risk-return index. This model is then extended in project portfolio selection, and as a result, a new model of R&D project portfolio selection is proposed under uncertainty. Constraints and limitations of R&D project portfolio selection are comprehensively addressed. In this model, lower semi-variance is applied to consider risk of proposed projects. Therefore, this paper offers a new model that applies IT2FSs to handle uncertainty, uses semi-variance to assess risk, synchronously considers risk and return in its selection process, and addresses the considerations and limits of realworld problems. Eventually, to verify the proposed model, a numerical example of the existing literature is solved with the model, and the results are compared. The first proposed model is used to prioritize proposed R&D projects of a gas and oil development holding firm as a real case study. To illustrate further, a practical example is also provided to demonstrate the applicability of the proposed project portfolio selection model.

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Fuzziness and randomness in investment project risk appraisal

Cited by 46 publications

References 24 publications

A Review on Risk Mitigation of IT Governance

A Review on Risk Mitigation of IT Governance

An integrated decision support approach for project investors in risky and uncertain environments

R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach

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