A Fuzzy DEA Approach for Project Selection Utilizing Analyze Desirable and Undesirable Risk
الموضوعات : مجله بین المللی ریاضیات صنعتی
SH. Sadeghiyan
1
(Department of Mathematics, Tabriz Branch, Islamic Azad University, Tabriz, Iran.)
F. Hosseinzade Lotfi
2
(Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
B. Daneshian
3
(Department of Mathematics, Central-Tehran Branch, Islamic Azad University, Tehran, Iran.)
N. Azarmir shotorbani
4
(Department of Mathematics, Tabriz Branch, Islamic Azad University, Tabriz, Iran.)
الکلمات المفتاحية: Downside Risk, Fuzzy theory, upper risk, Data Envelopment Analysis, project portfolio selection,
ملخص المقالة :
This paper proposes a DEA-based model for analyze the fuzzy risk in project selection. We used concept semi-variance for measure upper and downside risk and a DEA model for Classification desirable and undesirable risk. Firstly, the proposed model includes new desirable and undesirable risk-return indexes. Thus a novel DEA model is presented for evaluation and Classification desirable and undesirable risks and finally, is extend to fuzzy DEA model for project portfolio selection. An applied example is used to explain the proposed approach and usefulness and applicability of this approach have been illustrated using the 37 available projects.
[1] A. Amirteimori, M. Zadmirzaei, F. Hassanzadeh, Developing a new integrated artificial immune system and fuzzy non-discretionary DEA approach,Soft Computing 25 (2021) 8109-8127.
[2] F.J. Arteaga, A. Ebrahimnajad, A. Zabihi, A New Approach for Solving Intuitionistic Fuzzy Data Envelopment Analysis Problems, Fuzzy Optimization And Modelling 2 (2021) 46-57.
[3] A. Cheaitoua, R. Larbib, B. AlHousani, Decision making framework for tender evaluation and contractor selection in ublic organizations with risk considerations, SocioEconomic Planning Sciences 12 (2018) 1-12.
[4] Z. Chen, X. Ming, A rough-fuzzy approach integrating bestworst method and data envelopment analysis to multi-riteria selection of smart product service module, Appl. Soft Comput. 94 (2020) 106-129.
[5] W. Chen, Y. Gai, P. Gupta, Efficiency evaluation of fuzzy portfolio in different risk measures via DEA, Ann Oper Res. 14 (2016) 1-25.
[6] W. Chen, S. S. Li, J. Zhang, M. K. Mehlawat, A comprehensive model for fuzzy multi-objective portfolio selection based on DEA cross-efficiency model, Soft Computing 13 (2018) 1-12.
[7] W. D. Cook, R. H. Green, Project prioritization: A resource-constrained data envelopment analysis approach, Socio-conomic Planning Sciences 34 (2000) 85-99.
[8] A. Dadashi and B. Mirbaha, Prioritizing high way safety improvement projects: A Monte- Carlo based Data Envelopment Analysis approach, Accident Analysis and Prevention 123 (2019) 387-395.
[9] R. Dandage, Sh. S.Mantha, S. B. Rane, Ranking the risk categories in international projects using the TOPSIS method, International Journal of Managing Projects in Business 11 (2018) 317-331.
[10] A. Ebrahimnejad, N. Amani, Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points, Complex & Intelligent Systems 7 (2021) 379-400.
[11] A. Hatami-Marbini, S. Saati, Efficiency evaluation in two stage data envelopment analysis under a fuzzy environment: a commonweights approach, Appl Soft Comput 72 (2018) 156-165.
[12] N. G.Hall, D. Zhuoyu Long, J. Qi, M. Sim, Managing Under performance Risk in Project Portfolio Selection, Operations Research 63 (2015) 660-675.
[13] P. Gupta, MK. Mehlawat, A. Kumar, S. Yadav, A. Aggarwal, A Credibilistic Fuzzy DEA Approach for Portfolio Efficiency Evaluation and Rebalancing Toward Benchmark Portfolios Using Positive and Negative Returns, International Journal of Fuzzy Systems 32 (2020) 1-20.
[14] H. Jafarzadeh, P. Akbari, B. Abedin, A methodology for project portfolio selection under criteria prioritization uncertainty and projects interdependency combination of fuzzy QFD and DEA, Expert Systems with Application 110 (2018) 237-249.
[15] M. Kachouei, A. Ebrahimnejad, H. Bagherzadeh-Valami, A common-weights approach for efficiency evaluation in fuzzy data envelopment analysis with undesirable outputs: Application in banking industry, Journal of Intelligent & Fuzzy Systems 39 (2020) 7705-7722.
[16] J. Kettunen, A. Salo, Estimation of downside risks in project portfolio selection, Production and Operations Management 26 (2017) 1839-1853.
[17] X. Li, Z. Qin, Interval portfolio selection models within the framework of uncertainty theory, Economic Modelling 41 (2014) 338-344.
[18] P. Lio, B. Zhu, H. Seiti, L. Yang, Risk-based decision framework based on R-numbers and best-worst method and its application to research and development project selection, Information Sciences 571 (2021) 303-322.
[19] Z. Mashayekhi, H. Omrani, An integrated multi-objective Markowitz-DEA crossefficiency model with fuzzy returns for
portfolio selection problem, Applied Soft Computing 38 (2016) 1-9.
[20] SH. Nasseri, A. Ebrahimnejad, O. Gholami, Fuzzy stochastic data envelopment analysis with undesirable outputs and its application to banking industry, Int J Fuzzy Syst. 20 (2018) 534-548.
[21] H. Omrani, A. Alizadeh, A. Emrouznejad, T.Teplopa, A Robust Credibility DEA Model with Fuzzy Perturbation Degree: An Application to Hospitals Performance, Expert Systems with Applications, In Press.
[22] J. P. Paquin, C. Gauthier, P.P. Morin, The downside risk of project portfolios: The impact of capital investment projects and the value of project efficiency and project risk management programs, International Journal of project management 34 (2016) 1460-1470.
[23] P. Peykani, F. Hosseinzadeh Lotfi, S. J. ture review, current trends, and future directions, Fuzzy Optimization and Decision Making (2021). http://dx.doi.org/10.1007/s10700-021-09364-x/
[24] P. Peykani, E. Mohammadi, A. Emrouznejad, MS. Pishvaee, M. Rostamy-Malkhalifeh, Fuzzy data envelopment analysis: an adjustable approach, Expert Syst Appl. 136 (2019) 439-452.
[25] Z. Qin, S. Kar, H. Zheng, Uncertain portfolio adjusting model using semi-absolution deviation, Soft Computing 20 (2016) 717-725.
[26] A, Saeidifar, E, Pasha, The possibilistic moments of fuzzy numbers and their applications, Journal of Computational and Applied Mathematics 2 (2009) 1028-1042.
[27] S. Sadeghiyan, F. Hossseinzadeh-Lotfi, B. Daneshian, N. Azarmir-Shotorbani, A Model to Reduce the Risk of Project Selection utilizing Data envelopment analysis, Journal of modelling in management, (2021). Inpress. http://dx.doi.org/10.1108/JM2-08-2020-0220/
[28] Ch. Carlsson, R. Fullr, On possibilistic mean value and variance of fuzzy numbers, Fuzzy Sets and Systems 122 (2001) 315-326.
[29] Q. Shi, Y. Zhou, Ch. Xiao, R. Chen, J. Zuo, Delivery risk analysis within the context of program management using fuzzy logic and DEA: A China case study, International Journal of Project Management 32 (2014) 341-349.
[30] M. G. Speranza, Linear programming models for portfolio optimization, The Journal of Finance 12 (1993) 107-123.
[31] M. Tavana, M. Karampour, F. J. SantosArtega, E. Ghorbaniane, A fuzzy hybrid project portfolio selection method using data envelopment analysis, TOPSIS and Integer programming, Expert Systems with Application 42 (2015) 8432-8444
[32] M. Tavana, Gh. Khosrojerdi, H. Mina, A. Rahman, A hybrid mathematical programming model for optimal project portfolio selection using fuzzy inference system and analytic hierarchy process, Evaluation and Program Planning 77 (2019) 1-11.
[33] M. Toloo, M. Mirbolouki, A new project selection method using Data Envelopment Analysis, Computers & Industrial Engineering 21 (2019) 1-27.
[34] E. Vercher, J.D. Bermdez, J. Vicente Segura, Fuzzy portfolio optimization under downside risk measures, Fuzzy Sets and Systems 158 (2007) 769-782.
[35] E. Vercher, J. D. Bermdez, Portfolio optimization using a credibility mean-absolute semi-deviation model, Expert Systems with Applications 42 (2015) 7121-7131.
[36] Y. J. Wu, J. C. Chen, A structured method for smart city project selection, International Journal of Information Management 56 (2021) 1-9.
[37] A. E. Yamami, K. Mansouri, M. Qbadou, E. H. Illousamen, Multi objective IT project selection model for improving SME strategy deployment, International Journal of Electrical and Computer Engineering 8 (2018) 1102-1111.
[38] W. G. Zhang, Y. J. Liu, W. J. Xu, A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs, European Journal of Operational Research 222 (2012) 341-349.
[39] X. Zhang, L. Fang, K. W. Hipel, S. Ding, Y. Tan, A hybrid project portfolio selection procedure with historical performance consideration, Expert Systems With Applications 142 (2020) 1-28.
[40] Z. B. Zhou, H. L. Xiao, Q. Y. Jin, W. B. Liu, DEA frontier improvement and portfolio rebalancing: an application of china mutual funds on considering sustainability information disclosure, Eur. J. Oper. Res. 269 (2018) 111-131.
