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2000
Volume 2, Issue 1
  • ISSN: 2666-2949
  • E-ISSN: 2666-2957

Abstract

Introduction

In this study, a review of fuzzy implementation to Real Options Approach (ROA) theory where the applicability of classical and extended theories of “fuzziness” studied.

Background

ROA allows taking into account the value of some sources of managerial flexibility and therefore assessing a more accurately project value. The positive value of flexibility results from limiting the impacts of adverse events while taking advantage of positive ones. One of the main lessons is that uncertainty adds value in the presence of flexibility. Ambiguous parameters that have a significant effect on the project value are usually represented as fuzzy sets using Zadeh's classical theory of Fuzzy logic (also termed “type-1”). However, there have been so many derivatives, and expansions of the fuzzy set theories developed by different researchers. Dealing with uncertainty can be manifested in the different mechanism of fuzziness.

Objective

The objective of this review is to identify the research gap as well as provide an elementary guide to the applicability of different varieties of classical and extended applicability of fuzziness to ROA when evaluating project investment.

Methods

After a generic review of the progress of ROA theory and fuzzy approaches by researchers This paper reviews the applicability of ROA to fuzzy sets (classical and extended) implementation to decision-making for large projects where project timing and uncertainty are key parameters affecting the project value

Results

After reviewing the applicability of each of the classical and extended theories of fuzzy logic to ROA, a tabular format shows the result of this study summarizing the scenario, showing the applicability of different techniques.

Conclusion

Most of the reviewed techniques of fuzzy implementation to ROA approach, still based on the classical theory of fuzzy logic. Implementation of more extended techniques has a potential of enhancing the outcome of such research.

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2023-01-20
2025-01-19
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References

  1. ZadehL.A. Fuzzy sets.Inf. Control19658333835310.1016/S0019‑9958(65)90241‑X
    [Google Scholar]
  2. ZadehL.A. Outline of a new approach to the analysis of complex systems and decision processes.IEEE Trans. Syst. Man Cybern.19731284410.1109/TSMC.1973.5408575
    [Google Scholar]
  3. BennounaK. MeredithG.G. MarchantT. Improved capital budgeting decision making: Evidence from Canada.Manage. Decis.201048222524710.1108/00251741011022590
    [Google Scholar]
  4. GrahamJ.R. HarveyC.R. The theory and practice of corporate finance: Evidence from the field.J. Financ. Econ.200160218724310.1016/S0304‑405X(01)00044‑7
    [Google Scholar]
  5. GitmanL. ForresterJ. A survey of capital budgeting techniques used by major US firms.Financ. Manag.1977667110.2307/3665258
    [Google Scholar]
  6. BrennanM.J. SchwartzE.S. Evaluating natural resource investments.J. Bus.198558213510.1086/296288
    [Google Scholar]
  7. DixitA.K. PindyckR.S. Investment under uncertainty.Princeton university press199410.1515/9781400830176
    [Google Scholar]
  8. McDonaldR.L. SiegelD.R. Investment and the valuation of firms when there is an option to shut down.Int. Econ. Rev.198533134910.2307/2526587
    [Google Scholar]
  9. McDonaldR. SiegelD. The value of waiting to invest.Q. J. Econ.1986101470772710.2307/1884175
    [Google Scholar]
  10. TrigeorgisL. MasonS.P. Valuing managerial flexibility.Midland Corporate Finance Journal1987511421
    [Google Scholar]
  11. LuehrmanT.A. Strategy as a portfolio of real optionsHarv. Bus. Rev.199876589-9918710185434
    [Google Scholar]
  12. Ben AbdallahS. SicotteH. A real options analysis of project portfolios: Practitioners’ assessment.J. Mod. Project Manage201862
    [Google Scholar]
  13. BlackF. ScholesM. The pricing of options and corporate liabilities.J. Polit. Econ.197363765410.1086/260062
    [Google Scholar]
  14. MertonR. The Theory of Rational Option Pricing.Bell J. Econ. Manage. Sci.19734Spring14118310.2307/3003143
    [Google Scholar]
  15. LuehrmanT. Investment opportunities as real options: getting started on the numbers.Harv. Bus. Rev.19987645167
    [Google Scholar]
  16. YeoK. QiuF. The value of management flexibility-a real options approach to investment evaluation.Int. J. Project Manage.200321424325010.1016/S0263‑7863(02)00025‑X
    [Google Scholar]
  17. CarlssonC. FullérR. A fuzzy approach to real options valuation.Fuzzy Sets Syst.2003139229731210.1016/S0165‑0114(02)00591‑2
    [Google Scholar]
  18. ZmeškalZ. Application of the fuzzy-stochastic methodology to appraising the firm value as a European call option.Eur. J. Oper. Res.2001135230331010.1016/S0377‑2217(01)00042‑X
    [Google Scholar]
  19. WangJ. HwangW-L. A fuzzy set approach for R&D portfolio selection using a real options valuation model.Omega200735324725710.1016/j.omega.2005.06.002
    [Google Scholar]
  20. ChengJ-H. LeeC-Y. Product outsourcing under uncertainty: An application of fuzzy real options approach2007 IEEE International Fuzzy Systems Conference200710.1109/FUZZY.2007.4295675
    [Google Scholar]
  21. LiaoS-H. HoS-H. Investment project valuation is based on the fuzzy real options approach2010 International Conference on Technologies and Applications of Artificial Intelligence201010.1109/TAAI.2010.26
    [Google Scholar]
  22. SunT. GuoW. DaiH. Brownfield redevelopment evaluation based on fuzzy real.Sustainability201682170
    [Google Scholar]
  23. Ben AbdallahS. KouatliI. Fuzzy Volatility Effect on Major Projects Timing2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)201810.1109/FUZZ‑IEEE.2018.8491567
    [Google Scholar]
  24. RossT.J. Fuzzy logic with engineering applications.John Wiley & Sons2005
    [Google Scholar]
  25. SugenoM. An introductory survey of fuzzy control.Inf. Sci.1985361-2598310.1016/0020‑0255(85)90026‑X
    [Google Scholar]
  26. WangQ. KilgourD.M.W. HipelK. Facilitating risky project negotiation: An integrated approach using fuzzy real options, multicriteria analysis, and conflict analysis.Inf. Sci.20152952054455710.1016/j.ins.2014.10.049
    [Google Scholar]
  27. DongY. LiC.C. HerreraF. Connecting the linguistic hierarchy and the numerical scale for the 2-tuple linguistic model and its use to deal with hesitant unbalanced linguistic information.Inf. Sci.2016367-36825927810.1016/j.ins.2016.06.003
    [Google Scholar]
  28. ServatiY. GhodsypourS.H. ShiraziM.A. The use of fuzzy real options valuation method to rank Giga Investment Projects on Iran’s natural gas reserves.J. Fundam. Appl. Sci201791S
    [Google Scholar]
  29. Ben AbdallahS. KouatliI. Fuzzy Volatility of Project Option Value Based on Trapezoidal Membership FunctionsIntelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing.ChamSpringer2019102913071314
    [Google Scholar]
  30. KouatliI. AbdallahS.B. An Augmentation of Fuzziness to Randomness in Project Evaluation.Frontiers in Artificial Intelligence and Applications2018309
    [Google Scholar]
  31. MontajabihaM. KhamsehA.A. Afshar-NadjafiB. A robust algorithm for project portfolio selection problem using real options valuation.Int. J. Managing Proj. Bus.201710238640310.1108/IJMPB‑12‑2015‑0114
    [Google Scholar]
  32. GaoH. DingX-H. LiS. “EPC renewable project evaluation: A fuzzy real option pricing model”, Energy Sources, Part B.Econ. Plann. Policy2018139-10404413
    [Google Scholar]
  33. ZhouZ. LiuX. PeiJ. PardalosP. LiuL. FuC. Real options approach to explore the effect of organizational change on IoT development project.Optim. Lett.2017115995101110.1007/s11590‑016‑1006‑8
    [Google Scholar]
  34. TangW. CuiQ. ZhangF. ChenY. Urban rail-transit project investment benefits based on compound real options and trapezoid fuzzy numbers.J. Constr. Eng. Manage.20191451
    [Google Scholar]
  35. MendelJ. JohnR. LiuF. Interval type-2 fuzzy logic systems made simple.IEEE Trans. Fuzzy Syst.2006146808821
    [Google Scholar]
  36. TaiK. El-SayedA-R. BiglarbegianM. GonzalezC. CastilloO. MahmudS. Review of Recent Type-2 Fuzzy Controller Applications.Algorithms201693910.3390/a9020039
    [Google Scholar]
  37. LiangQ. MendelJ.M. Interval Type-2 fuzzy logic systems: Theory and design.IEEE Trans. Fuzzy Syst.20001853555010.1109/91.873577
    [Google Scholar]
  38. SemerciogluN. TolgaA.C. A multi-stage new product development using fuzzy Type-2 sets in a real options valuationIEEE International Conference on Fuzzy Systems (FUZZ-IEEE)201610.1109/FUZZ‑IEEE.2015.7338043
    [Google Scholar]
  39. TolgaA.C. Real options valuation of an IoT based healthcare device with interval Type-2 fuzzy numbers.Socioecon. Plann. Sci.20192019
    [Google Scholar]
  40. LagunesM.L. CastilloO. ValdezF. SoriaJ. Comparative Study of Fuzzy Controller Optimization with Dynamic Parameter Adjustment Based on Type 1 and Type 2 Fuzzy Logic. Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing.ChamSpringer2019100010.1007/978‑3‑030‑21920‑8_27
    [Google Scholar]
  41. SanchezM.A. CastilloO. CastroJ.R. Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems.Expert Syst. Appl.201542145904591410.1016/j.eswa.2015.03.024
    [Google Scholar]
  42. ZarandiM.H.F. SoltanzadehS. MohammadiA. CastilloO. Designing a general type-2 fuzzy expert system for diagnosis of depression.Appl. Soft Comput.201980July32934110.1016/j.asoc.2019.03.027
    [Google Scholar]
  43. CastilloO. Bio-inspired Optimization of Type-2 Fuzzy Controllers in Autonomous Mobile Robot Navigation.Advanced Control Techniques in Complex Engineering Systems: Theory and Applications. Studies in Systems, Decision, and Control.ChamSpringer201920310.1007/978‑3‑030‑21927‑7_9
    [Google Scholar]
  44. AtanassovK. More on Intuitionistic Fuzzy Sets.Fuzzy Sets Syst.1986201879610.1016/S0165‑0114(86)80034‑3
    [Google Scholar]
  45. TorraV. Hesitant Fuzzy sets.International Journal of Intelligent Systems256529539, 20102010
    [Google Scholar]
  46. Huseyin Yigit ErsenH. TasO. KahramanC. Intuitionistic Fuzzy Real-Options Theory and its Application to Solar Energy Investment Projects.Eng. Econ.2018292140150
    [Google Scholar]
  47. XuZ. ZhaoN. Information fusion for intuitionistic fuzzy decision making: An overview.Inf. Fusion201628March102310.1016/j.inffus.2015.07.001
    [Google Scholar]
  48. DongY. ChenX. HerreraF. Minimizing adjusted simple terms in the consensus reaching process with hesitant linguistic assessments in group decision making.Inf. Sci.20152979511710.1016/j.ins.2014.11.011
    [Google Scholar]
  49. DongM-G. Li, Shou-Yi (2016) Project investment decision making with fuzzy information: A literature review of methodologies based on taxonomy.J. Intell. Fuzzy Syst.20163063239325210.3233/IFS‑152068
    [Google Scholar]
  50. ZhangH. DongY. FranciscoC. YuS. Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design.Eur. J. Oper. Res.2019275258059810.1016/j.ejor.2018.11.052
    [Google Scholar]
  51. ZhanJ. MalikH.M. AkramM. Novel decision-making algorithms based on intuitionistic fuzzy rough environment.International Journal of Machine Learning and Cybernetics201810614591485
    [Google Scholar]
  52. MezeiJ. CollanM. LuukkaP. Real options analysis with interval-valued fuzzy numbers and the fuzzy pay-off methodProceedings of the Conference of the European Society for Fuzzy Logic and Technology International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets: Advances in Fuzzy Logic and Technology6412017509520
    [Google Scholar]
  53. YeniF.B. ÖzçelikG. Interval-valued atanassov intuitionistic fuzzy codas method for multi-criteria group decision making problems.Group Decis. Negot.201828243345210.1007/s10726‑018‑9603‑9
    [Google Scholar]
  54. GargH. RaniD. Some generalized complex intuitionistic fuzzy aggregation operators and their application to multicriteria decision-making process.Arab. J. Sci. Eng.20194432679269810.1007/s13369‑018‑3413‑x
    [Google Scholar]
  55. CastilloO. MelinP. TsvetkovR. AtanassovK.T. short remark on fuzzy sets, interval type-2 fuzzy sets, general type-2 fuzzy sets, and intuitionistic fuzzy setsIntelligent Systems’2014. Advances in Intelligent Systems and ComputingChamSpringer201532210.1007/978‑3‑319‑11313‑5_18
    [Google Scholar]
  56. CastilloO. AtanassovK. Comments on fuzzy sets, interval type-2 fuzzy sets, general type-2 fuzzy sets, and intuitionistic fuzzy sets. Recent Advances in Intuitionistic Fuzzy Logic Systems. Studies in Fuzziness and Soft Computing.ChamSpringer201937210.1007/978‑3‑030‑02155‑9_3
    [Google Scholar]
  57. YagerR.R. Pythagorean membership grades in multicriteria decision making.IEEE Trans. Fuzzy Syst.201422495896510.1109/TFUZZ.2013.2278989
    [Google Scholar]
  58. ZhangX. XuZ. Extension of TOPSIS to Multiple CriteriaDecision Making with Pythagorean Fuzzy Sets.Int. J. Intell. Syst.2014291061107810.1002/int.21676
    [Google Scholar]
  59. GargH. A novel accuracy function under interval-valued Pythagorean fuzzy environment for solving multicriteria decision making problem.J. Intell. Fuzzy Syst.201631152954010.3233/IFS‑162165
    [Google Scholar]
  60. MaoL. GuiwuW. Alsaadi, Fuad E., Tasawar, H., Alsaedi, A. (2017) Hesitant Pythagorean fuzzy Hamacher aggregation operators and their application to multiple attribute decision-making.J. Intell. Fuzzy Syst.20173321105111710.3233/JIFS‑16554
    [Google Scholar]
  61. OnarS.C. OztaysiB. KahramanC. “Multicriteria evaluation of cloud service providers using pythagorean fuzzy TOPSIS”, J. Multiple-Valued Log.Soft Comput.2018302-3263283
    [Google Scholar]
  62. KahramanC. OnarS.C. OztaysiB. Present worth analysis using pythagorean fuzzy setsProceedings of the Conference of the European Society for Fuzzy Logic and Technology International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets EUSFLAT 2017, IWIFSGN 2017: Advances in Fuzzy Logic and Technology2017336342
    [Google Scholar]
  63. PengX. Pythagorean fuzzy set: State of the art and future directions.Artificial Intelligence Review201715510.1007/s10462‑017‑9596‑9
    [Google Scholar]
  64. IlbaharE. KaraşanA. CebiS. KahramanC. A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system.Saf. Sci.2018103March12413610.1016/j.ssci.2017.10.025
    [Google Scholar]
  65. SharmaH. A hybrid pythagorean group decision making model for website selection.Multi-Criteria Decision-Making Models for Website Evaluation201910.4018/978‑1‑5225‑8238‑0.ch004
    [Google Scholar]
  66. DorfeshanY. MousaviS.M. A group TOPSIS-COPRAS methodology with Pythagorean fuzzy sets considering weights of experts for project critical path problem.J. Intell. Fuzzy Syst.20193621375138710.3233/JIFS‑172252
    [Google Scholar]
  67. SmarandacheF. Neutrosophy: Neutrosophic probability, set, and logic.Rehoboth, USAAmer. Res. Press19981105
    [Google Scholar]
  68. BroumiS. BakaliA. TaleaM. SmarandacheF. An Isolated Interval Valued Neutrosophic Graph.Crit. Rev.2016136780
    [Google Scholar]
  69. Kutlu GündoğduF. KahramanC. Spherical fuzzy sets and spherical fuzzy TOPSIS method.J. Intell. Fuzzy Syst.201936133735210.3233/JIFS‑181401
    [Google Scholar]
  70. ZhangW.R. Bipolar fuzzy sets and relations: A computational framework for cognitive modeling and multiagent decision analysisFuzzy Information Processing Society Biannual Conference1994
    [Google Scholar]
  71. MolodtsovD.A. Soft set theory-_rst results, computers and mathematics with applications.Computers and Mathematics with Applications1999374-51931
    [Google Scholar]
  72. MajiP.K. RoyA.R. BiswasR. Fuzzy soft sets.J. Fuzzy Math.200193589602
    [Google Scholar]
  73. SinghP.K. KumarA.Ch. A note on a bipolar fuzzy graph representation of concept lattice.Int. J. Comput. Sci. Math.20145438139310.1504/IJCSM.2014.066426
    [Google Scholar]
  74. RashmanlouH. SamantaS. PalM. BorzooeiR.A. Bipolar fuzzy graphs with categorical properties.Int. J. Computat. Intell. Syst.20158580881810.1080/18756891.2015.1063243
    [Google Scholar]
  75. ShahzadiS. AkramM. Edge regular intuitionistic fuzzy soft graphs.J. Intell. Fuzzy Syst.20163131881189510.3233/JIFS‑16120
    [Google Scholar]
  76. SarwarM. AkramM. Novel concepts bipolar fuzzy competition graphs.J. Appl. Math. Comput.2017541-251154710.1007/s12190‑016‑1021‑z
    [Google Scholar]
  77. AkramM. NawazS. Fuzzy soft graphs with applications.Journal of intelligent and fuzzy systems3036193632, 20162016
    [Google Scholar]
  78. KaufmanA. Introduction a la Theorie des Sous-ensembles Flous.Masson et Cie1973
    [Google Scholar]
  79. AkramM. FengF. Borumand SaeidA. And Leoreanu-FoteaV. “New Multiple Criteria Decision-Making Method Based On Bipolar Fuzzy Soft Graphs”, Iran.J. Fuzzy Syst.20181547392
    [Google Scholar]
  80. KouatliI. “Fuzzimetric Sets: An Integrated Platform for Both Types of Interval Fuzzy Sets”, Frontiers in Artificial Intelligence and Applications, Vol.Fuzzy Systems and Data Mining2018IV150163
    [Google Scholar]
  81. KouatliI. Fuzziness Control of Fuzzimetric sets, FUZZ-IEEE2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)201915
    [Google Scholar]
  82. KouatliI. Fuzzimetric employee evaluations system (FEES): A multivariable-modular approach.J. Intell. Fuzzy Syst.2018354717472910.3233/JIFS‑181202
    [Google Scholar]
  83. KouatliI. The Use of Fuzzy Logic as Augmentation to Quantitative Analysis to Unleash Knowledge of Participants’ Uncertainty when Filling a Survey: Case of cloud computing.IEEE Transactions on Knowledge and Data Engineering202010.1109/TKDE.2020.2993326
    [Google Scholar]
  84. KouatliI Modeling fuzzimetric cognition of technical analysis decisions: Reducing emotional tradingJ. Fuzzy Ext. Appl.2022
    [Google Scholar]
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