Application of Gray Systems and Fuzzy Sets in Combination with Real Options Theory in Project Portfolio Management

Arasteh, Abdollah; Aliahmadi, Alireza; Omran, Mohammad Mohammadpour

doi:10.1007/s13369-014-1155-y

Cited by 10 publications

(11 citation statements)

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“…Hoeck [21] applied the traditional approach of consideration of the target ROI to transformation of a revenue management technique popular with the airline industry for application to project selection and scheduling problems. Arasteh et al [22] applied the gray system and fuzzy set theories with the real options theory for project portfolio management. Their model addressed the problem of project portfolio selection and presented some approaches for evaluating investment portfolios with an evaluation tool.…”

Section: Economic Methodsmentioning

confidence: 99%

“…Then, the importance of each criterion is compared to construct the local weights in the super-matrix. For example, domain experts were requested to reply a series of questions, such as "For the probability of technical success, how much more important is manufacturing capability (C 21 ) over facility and equipment requirements (C 22 )." They were asked to score pairwise comparisons on the nine-point scale, ranging from 1 indicating equal importance to 9 indicating extreme importance.…”

Section: Weighting Of Criteria For Randd Project Selectionmentioning

confidence: 99%

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A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

2017

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In today's rapidly changing economy, technology companies have to make decisions on research and development (R&D) projects investment on a routine bases with such decisions having a direct impact on that company's profitability, sustainability and future growth. Companies seeking profitable opportunities for investment and project selection must consider many factors such as resource limitations and differences in assessment, with consideration of both qualitative and quantitative criteria. Often, differences in perception by the various stakeholders hinder the attainment of a consensus of opinion and coordination efforts. Thus, in this study, a hybrid model is developed for the consideration of the complex criteria taking into account the different opinions of the various stakeholders who often come from different departments within the company and have different opinions about which direction to take. The decision-making trial and evaluation laboratory (DEMATEL) approach is used to convert the cause and effect relations representing the criteria into a visual network structure. A consistent fuzzy preference relations based analytic network process (CFPR-ANP) method is developed to calculate the preference-weights of the criteria based on the derived network structure. The CFPR-ANP is an improvement over the original analytic network process (ANP) method in that it reduces the problem of inconsistency as well as the number of pairwise comparisons. The combined complex proportional assessment (COPRAS-G) method is applied with fuzzy grey relations to resolve conflicts arising from differences in information and opinions provided by the different stakeholders about the selection of the most suitable R&D projects. This novel combination approach is then used to assist an international brand-name company to prioritize projects and make project decisions that will maximize returns and ensure sustainability for the company.

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Section: Economic Methodsmentioning

confidence: 99%

Section: Weighting Of Criteria For Randd Project Selectionmentioning

confidence: 99%

A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

2017

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“…Portfolio Management [13] They consider all possible uncertain conditions that a project can encounter and express their methods for each of these situations. They used two applicable approaches in different situations of uncertainty.…”

Section: Application Of Gray Systems and Fuzzy Sets In Combination Wimentioning

confidence: 99%

A Survey of Grey Systems Applied to Multi-objective Problem

Balderas

Fernández

Gómez

et al. 2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

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Decision-making processes involve a series of steps: identifying the problems, constructing the preferences, evaluating the alternatives, and determining the best alternatives (Simon, The new science of management decision. In: Proceedings of the 33rd Conference of theOperational Research Society of New Zealand, Citeseer, 1960). Using a multi-criteria decision making approach, the grey system theory has been used to capture the complexity inherent in selection process. The grey system theory proposed by Deng (J. Grey Syst. 1(1), 1-24 1989) is based on the assumption that a system is uncertain and that the information regarding the system is insufficient to build a relational analysis or to construct a model to characterize the system. The aim of this chapter is to provide a short review of the three mostly seen research methods employed for the investigation of uncertain systems: probability and statistics, fuzzy mathematics and grey systems theory. This chapter provides a short review of the general framework, current research trends and future research topics on grey systems applied to decision problems.

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“…There exist numerous methods for investment evaluation that are currently in use, which aim to provide decision makers with sufficient information for making investment decisions, including traditional valuation methods and The associate editor coordinating the review of this manuscript and approving it for publication was Alba Amato. real options (RO) methods [2], [3]. Traditional valuation methods that are based on discounted cash flows (DCF) analysis provide measures such as net present value (NPV), internal rate of return (IRR), payback and maximum cash exposure [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Application of Real Options on the Decision-Making of Mining Investment Projects Using the System Dynamics Method

Liu

et al. 2019

IEEE Access

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Estimating the valuation of mining projects has great significance for investment decisionmaking. Since the existing valuation methods are not appropriate for mining projects affected by various uncertainties over the long term, this paper presents a hybrid investment evaluation model that is based on real options and system dynamics for mining projects. To address uncertainties and managerial flexibilities that are contained in mining projects, the model integrates the static net present value calculated by traditional discount cash flows and the real options value generated by the Black-Scholes model into the total value of mining projects. Subsequently, the model applies system dynamics modeling to analyze the dynamics of the complicated mining operation system, quantify its variables and interactions, and estimate its volatility, which enables a more accurate real options valuation. A realistic case of the Hongwei uranium deposit in China is the basis for the presented numerical illustration of the model. The findings indicate that investors are prone to losing an opportunity to invest if they only rely on traditional discount cash flows' methods, because the value of the project calculated by discount cash flows is −175.59 million yuan that under zero. However, the results obtained from the method proposed in this paper suggest that the project has a value of 327.65 million yuan.INDEX TERMS Decision making, estimation, modeling, real options, system dynamics.

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Application of Gray Systems and Fuzzy Sets in Combination with Real Options Theory in Project Portfolio Management

Cited by 10 publications

References 50 publications

A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

A Survey of Grey Systems Applied to Multi-objective Problem

Application of Real Options on the Decision-Making of Mining Investment Projects Using the System Dynamics Method

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