[1] M. Asadujjaman and K. Zaman, Robustness-based portfolio optimization under epistemic uncertainty, J. Ind. Eng. Int. 15 (2019), 207–219.
[2] C. Baudrit, D. Dubois and D. Guyonnet, Joint propagation and exploitation of probabilistic and possibilistic information in risk assessment, IEEE Trans. Fuzzy Syst. 14 (2006), no. 5, 593–608.
[3] A.T. Beck, W.J. Gomes and F.A. Bazgn, On the robustness of structural risk optimization with respect to epistemic uncertainties, Int. J. Uncertain. Quantific.2 (2012), no. 1.
[4] Y. Ben-Haim, Info-gap decision theory: decisions under severe uncertainty, Elsevier, 2006.
[5] D. Berleant, L. Andrieu, J.P. Argaud, F. Barjon, M.P. Cheong, M. Dancre, M., . . . and C.C. Teoh, Portfolio management under epistemic uncertainty using stochastic dominance and information-gap theory, Int. J. Approx. Reason. 49 (2008), no. 1, 101–116.
[6] V. Boasson, E. Boasson and Z. Zhou, Portfolio optimization in a mean-semivariance framework, Invest. Manag. Financ. Innov. 8 (2011), no. 3, 58–68.
[7] G. Bormetti, M.E. De Giuli, D. Delpini and C. Tarantola, Bayesian value-at-risk with product partition models, Quant. Finance 12 (2012), no. 5, 769–780.
[8] G.W. Brown and M.T. Cliff, Investor sentiment and the near-term stock market, J. Empir. Finance 11 (2004), no. 1, 1–27.
[9] L.A.S. Camargo, L.D. Leonel, D.S. Ramos and A.G.D. Stucchi, A risk averse stochastic optimization model for wind power plants portfolio selection, Int. Conf. Smart Energy Syst. Technol., IEEE, 2020), pp. 1–6.
[10] Z. Chen and P.J. Knez, Portfolio performance measurement: Theory and applications, Rev. Financ. Stud. 9 (1996), no. 2, 511–555.
[11] M.P. Cheong, G.B. Sheble, D. Berleant, C.C. Teoh, J.P. Argaud, M. Dancre, L. Andrieu and F. Barjon, Second order stochastic dominance portfolio optimization for an electric energy company, IEEE Lausanne Power Tech. 2007 (2007), 819–824.
[12] L.B. Chincarini, Quantitative equity portfolio management: An active approach to portfolio construction and management, McGraw-Hill, 2006.
[13] Z. Dai, D. Li and F. Wen, Worse-Case Conditional Value-at-Risk for Asymmetrically Distributed Asset Scenarios Returns, J. Comput. Anal. Appl. 20 (2016), no. 1.
[14] E. Delage and Y. Ye, Distributionally robust optimization under moment uncertainty with application to datadriven problems, Oper. Res.58 (2010), no. 3, 595–612.
[15] D. Desai, G. Wu and M.H. Zaman, Tackling HIV through robust diagnostics in the developing world: current status and future opportunities, Lab Chip 11 (2011), no. 2, 194–211.
[16] M. Esmaili, N. Amjady and H.A. Shayanfar, Multi-objective congestion management by modified augmented εconstraint method, Appl. Energy 88 (2011), no. 3, 755–766.
[17] M. Fereiduni and K. Shahanaghi, A robust optimization model for distribution and evacuation in the disaster response phase, J. Ind. Engin. Int. 13 (2017), no. 1, 117–141.
[18] A. Fertis, M. Baes and H.J. L¨uthi, Robust risk management, Eur. J. Oper. Res. 222 (2012), no. 3, 663–672.
[19] A. Ghadimi Hamzehkolaei, G. Ghodrati Amiri, A. Gharagozlu, A. Vafaeinezhad and A. Zare Hosseinzadeh, Seismic zoning of urban areas considering the effect of physical conditions using Fuzzy logic theory: case study of Tehran’s 7th region, J. Struct. Construct. Engin. 5 (2018), no. 3, 5–15.
[20] J.W. Goh, K.G. Lim, M. Sim and W. Zhang, Portfolio value-at-risk optimization for asymmetrically distributed asset returns, Eur. J. Oper. Res. 221 (2012), no. 2, 397–406.
[21] A. Hafezalkotob, A. Hami-Dindar, N. Rabie and A. Hafezalkotob, A decision support system for agricultural machines and equipment selection: A case study on olive harvester machines, Comput. Electron. Agricul. 148 (2018), 207–216.
[22] J.C. Helton, Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal, Reliab. Engin. Syst. Safety 42 (1993), no. 2-3, 327–367.
[23] B.L. Hu and E. Verdaguer, Stochastic gravity: Theory and applications, Liv. Rev. Relat. 11 (2008), no. 1, 1–112.
[24] G.B. Huang, Q.Y. Zhu and C.K. Siew, Extreme learning machine: theory and applications, Neurocomput. 70 (2006), no. 1-3, 489–501.
[25] H. Jin, Z. Quan Xu and X. Yu Zhou, A convex stochastic optimization problem arising from portfolio selection, Math. Finance: Int. J. Math. Statist. Financ. Econ. 18 (2008), no. 1, 171-183.
[26] A.R. Jordehi, M.S. Javadi, M. Shafie-khah and J.P. Catallo, Information gap decision theory (IGDT)-based robust scheduling of combined cooling, heat and power energy hubs, Energy 231 (2021), 120918.
[27] N. Khalaj, N.A. Abu Osman, A.H. Mokhtar, M. Mehdikhani and W.A.B. Wan Abas, Balance and risk of fall in individuals with bilateral mild and moderate knee osteoarthritis, PloS one 9 (2014), no. 3, 92270.
[28] H. Konno and T. Koshizuka, Mean-absolute deviation model, Iie Trans. 37 (2005), no. 10, 893–900.
[29] M. Labb´e, P. Marcotte and G. Savard, A bilevel model of taxation and its application to optimal highway pricing, Manag. Sci. 44 (1998), no. 12-part-1, 1608–1622.
[30] G. F. Loewenstein, E. U. Weber, Ch. K. Hsee and N. Welch, Risk as feelings, Psych. Bull. 127 (2001), no. 2, 267.
[31] V.W. Lui, M.L. Hedberg, H. Li, B.S. Vangara, K. Pendleton, Y. Zeng, . . . and J.R. Grandis, Frequent Mutation of the PI3K Pathway in Head and Neck Cancer Defines Predictive BiomarkersMutation of PI3K Pathway in Head and Neck Cancer, Cancer Discov. 3 (2013), no. 7, 761–769.
[32] M. Majidi, B. Mohammadi-Ivatloo and A. Soroudi, Application of information gap decision theory in practical energy problems: A comprehensive review, Appl. Energy 249 (2019), 157–165.
[33] H. Markowitz, The utility of wealth, J. Ppolitic. Econ. 60 (1952), no. 2, 151–158.
[34] W. Markowitz, Variations in rotation of the earth, results obtained with the dual-rate moon camera and photographic zenith tubes, Symp. Int. Astronom. Union, Cambridge University Press, 11 (1959), 26–33.
[35] M. Mehrbod, N. Tu and L. Miao, A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty, J. Ind. Engin. Int. 11 (2015), no. 2, 237–252.
[36] Y. Merzifonluoglu, Risk averse supply portfolio selection with supply, demand and spot market volatility, Omega 57 (2015), 40–53.
[37] A. Meucci, Risk and asset allocation, Springer, New York, 2005.
[38] W.L. Oberkampf, S.M. DeLand, B.M. Rutherford, K.V. Diegert and K.F. Alvin, Error and uncertainty in modeling and simulation, Reliab. Engin. Syst. Safety 75 (2002), no. 3, 333–357.
[39] N.P. O’Dowd, Y. Lei and E. P. Busso, Prediction of cleavage failure probabilities using the Weibull stress, Engin.Fracture Mech. 67 (2000), no. 2, 87–100.
[40] Z. Qin, S. Kar and H. Zheng, Uncertain portfolio adjusting model using semiabsolute deviation, Soft Comput. 20 (2016), no. 2, 717–725.
[41] R.T. Rockafellar and S. Uryasev, Optimization of conditional value-at-risk, J. Risk 2 (2000), 21–42.
[42] S.A. Ross, The determination of financial structure: the incentive-signalling approach, Bell J. Econ. 8 (1977), no. 1, 23–40.
[43] P.A. Samuelson, Lifetime portfolio selection by dynamic stochastic programming, Stochastic Optimization Models in Finance (1975), 517–524.
[44] S. Sarykalin, G. Serraino and S. Uryasev, Value-at-risk vs. conditional value-at-risk in risk management and optimization, State-of-the-art decision-making tools in the information-intensive age, Informs (2008), 270–294.
[45] C. Skiadas, Dynamic portfolio choice and risk aversion, Oper. Res- Manag. Sci. 15 (2007), 789–843.
[46] W. F. Sharpe, Mean-absolute-deviation characteristic lines for securities and portfolios, Manag. Sci. 18 (1971), no. 2, B-1.
[47] T. Sriyakul and K. Jermsittiparsert, Risk-constrained design of autonomous hybrid refueling station for hydrogen and electric vehicles using information gap decision theory, Int. J. Hydrogen Energy 46 (2021), no. 2, 1682-1693.
[48] R. S. Tsay, Analysis of financial time series, John wiley & sons, 2005.
[49] S. Uryasev and P. M. Pardalos, Stochastic optimization: algorithms and applications, Springer Sci. Bus. Media 54 (2013).
[50] Z. Vafaeinezhad, Z. Kazemi, M. Mirmoeini, H. Piroti, E. Sadeghian, M. Mohammad Ali-Vajari, . . . and M. Jafari, Trends in cervical cancer incidence in Iran according to national cancer registry, J. Mazandaran Univer. Med. Sci.28 (2018), no. 161, 108–114.
[51] S. Yoo, S. Jeon, S. Jeong, H. Lee, H. Ryou, T. Park, . . . and K. Oh, Prediction of the change points in stock markets using DAE-LSTM, Sustainability 13 (2021), no. 21, 11822.
[52] S. Zaman and D. Grosu, Combinatorial auction-based allocation of virtual machine instances in clouds, J. Paral. Distrib. Comput. 73 (2013), no. 4, 495–508.
[53] S. Zymler, D. Kuhn and B. Rustem, Worst-case value at risk of nonlinear portfolios, Manag. Sci. 59 (2013), no. 1, 172- 188.
