1. Atanassov, K. T. (2008). My personal view on intuitionistic fuzzy sets theory. InFuzzy Sets and Their Extensions: Representation, Aggregation & Models (pp. 23-43). Springer Berlin Heidelberg. 
                                                                                                                2. Atanassov, K. T. Intuitionistic fuzzy sets. Central Tech Library, Bulgarian Academy Science, Sofia, Bulgaria, 1983.
                                                                                                                3. Atkinson, R., Crawford, L., & Ward, S. (2006). Fundamental uncertainties in projects and the scope of project management. International journal of project management, 24(8), 687-698. 
                                                                                                                4. Barbosa, P. S., & Pimentel, P. R. (2001). A linear programming model for cash flow management in the Brazilian construction industry. Construction management and Economics, 19(5), 469-479. 
                                                                                                                5. Bhattacharyya, R. (2015). A Grey Theory Based Multiple Attribute Approach for R&D Project Portfolio Selection.
                                                                                                                Fuzzy Information and Engineering, 7(2), 211-225. 
                                                                                                                6. Blyth, K. & Kaka, A. (2006). A novel multiple linear regression model for forecasting S-curves, Engineering, Construction and Architectural Management, 13(1): 82–95. 
                                                                                                                7. Boran, F. E., Boran, K., & Menlik, T. (2012). The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS. Energy Sources, Part B: Economics, Planning, and Policy, 7(1), 81-90. 
                                                                                                                8. Boussabaine A.H. & Kaka, A. (1998). A neural networks approach for cost-flow forecasting. Construction Management and Economics Journal, 16, 471-479. 
                                                                                                                9. Caron, F., & Comandulli, M. (2014). A cash flow-based approach for assessing expansion options stemming from project modularity. International Journal of Project Organization and Management, 6(1-2), 157-178. 
                                                                                                                10. Chai, J., Liu, J. N., & Xu, Z. (2012). A new rule-based SIR approach to supplier selection under intuitionistic fuzzy environments. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 20(3), 451-471. 
                                                                                                                11. Chanas, S., & Kamburowski, J. (1981). The use of fuzzy variables in PERT. Fuzzy sets and systems, 5(1), 11-19. 
                                                                                                                12. Chen, C. C., & Zhang, Q. (2014). Applying quality function deployment techniques in lead production project selection and assignment. In Advanced Materials Research (Vol. 945, pp. 2954-2959). 
                                                                                                                13. Chen, H. L., Chen, C. I., Liu, C. H., & Wei, N. C. (2013). Estimating a project's profitability: A longitudinal approach.
                                                                                                                International Journal of Project Management, 31(3), 400-410. 
                                                                                                                14. Cheng, M. Y., & Roy, A. F. (2011). Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines. International Journal of Project Management, 29(1), 56-65. 
                                                                                                                15. Cheng, M. Y., Hoang, N. D., and Wu, Y. W. (2015). Cash flow prediction for construction project using a novel adaptive time-dependent least squares support vector machine inference model. Journal of Civil Engineering and Management, 21(6), 679-688.
                                                                                                                16. Cioffi, D.F., (2005). A tool for managing projects: an analytic parameterization of the S-curve. International Journal of Project Management, 23(3), 215–222. 
                                                                                                                17. Cooke, B., & Jepson, W. B. (1979). Cost and financial control for construction firms. Macmillan. 
                                                                                                                18. Deng, H. (2014). Comparing and ranking fuzzy numbers using ideal solutions. Applied Mathematical Modelling, 38(5), 1638-1646. 
                                                                                                                19. Duong, A. N. (2011). Rate-decline analysis for fracture-dominated shale reservoirs. SPE Reservoir Evaluation and Engineering, 14(3), 377.
                                                                                                                20. Gerogiannis, V. C., Fitsilis, P., & Kameas, A. D. (2011). Using a combined intuitionistic fuzzy set-TOPSIS method for evaluating project and portfolio management information systems. In Artificial Intelligence Applications and Innovations (pp. 67-81), Springer Berlin Heidelberg. 
                                                                                                                21. Gormley, F.M., & Meade, N., 2007. The utility of cash flow forecasts in the management of corporate cash balances.
                                                                                                                European Journal of Operational Research 182(2), 923–935 .
                                                                                                                22. Hsu, K. (2003). Estimation of a double S-curve model, AACE International Transactions IT13.1– IT13.5.
                                                                                                                23. Hwee, N. G. & Tiong, R. L. K., (2002). Model on cash flow forecasting and risk analysis for contracting firms, International Journal of Project Management, 20, 351-363. 
                                                                                                                24. Jarrah, R., Kulkarni, D., & O’Connor, J.T., (2007). Cash flow projections for selected TxDoT highway projects. Journal of Construction Engineering and Management, 133(3), 235–241. 
                                                                                                                25. Jiang, A., Issa, R. R., & Malek, M. (2011). Construction project cash flow planning using the Pareto optimality efficiency network model. Journal of Civil Engineering and Management, 17(4), 510-519. 
                                                                                                                26. Khosrowshahi, F., & Kaka, A. P. (2007). A decision support model for construction cash flow management. Computer‐Aided Civil and Infrastructure Engineering, 22(7), 527-539. 
                                                                                                                27. Kumar, V. S., Hanna, A. S., & Adams, T. (2000). Assessment of working capital requirements by fuzzy set theory.
                                                                                                                Engineering, Construction and Architectural Management, 7(1), 93-103. 
                                                                                                                28. Lam, K. C., et al. (2001). An integration of the fuzzy reasoning technique and the fuzzy optimization method in construction project management decision-making. Construction Management and Economics, 19(1), 63-76. 
                                                                                                                29. Lawson, C. P., Longhurst, P. J., & Ivey, P. C. (2006). The application of a new research and development project selection model in SMEs. Technovation, 26(2), 242-250. 
                                                                                                                30. Lee F. (1998). Fuzzy information processing system. Peking University Press Inc., 118–132. 31. Li, H., & Yen, V. C. (1995). Fuzzy sets and fuzzy decision-making. CRC press. 
                                                                                                                32. Liang, C., Zhao, S., & Zhang, J. (2014). Aggregation Operators on Triangular Intuitionistic Fuzzy Numbers and its Application to Multi-Criteria Decision Making Problems. Foundations of Computing and Decision Sciences, 39(3), 189-208. 
                                                                                                                33. Maravas, A., & Pantouvakis, J. P. (2012). Project cash flow analysis in the presence of uncertainty in activity duration and cost. International journal of project management, 30(3), 374-384.
                                                                                                                34. McCahon, C. S., & Lee, E. S. (1988). Project network analysis with fuzzy activity times. Computers & Mathematics with applications, 15(10), 829-838. 
                                                                                                                35. Mohagheghi, V., Mousavi, S. M., & Vahdani, B. (2015). A new optimization model for project portfolio selection under interval-valued fuzzy environment. Arabian Journal for Science and Engineering, 40, 3351–3361. 
                                                                                                                36. Mousavi, S. M., Jolai, F., & Tavakkoli-Moghaddam, R. (2013). A fuzzy stochastic multi-attribute group decision-making approach for selection problems. Group Decision and Negotiation, 22(2), 207-233. 
                                                                                                                37. Neog, T. J., & Sut, D. K. (2011). An application of fuzzy soft sets in medical diagnosis using fuzzy soft complement.
                                                                                                                International Journal of Computer Applications, 33(9). 
                                                                                                                38. Ning, X., Lam, K. C., & Lam, M. C. K. (2011). A decision-making system for construction site layout planning. Automation in Construction, 20(4), 459-473. 
                                                                                                                39. Prade, H. (1979). Using fuzzy set theory in a scheduling problem: a case study. Fuzzy sets and systems, 2(2), 153-165. 
                                                                                                                40. Rostamy, A. A., Takanlou, F., & AnvaryRostamy, A. (2013). A fuzzy statistical expert system for cash flow analysis and management under uncertainty. Advances in Economics and Business, 1(2), 89-102. 
                                                                                                                41. Santamaría, L., Barge-Gil, A., & Modrego, A. (2010). Public selection and financing of R&D cooperative projects: Credit versus subsidy funding. Research Policy, 39(4), 549-563. 
                                                                                                                42. Shu, M. H., Cheng, C. H., & Chang, J. R. (2006). Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly. Microelectronics Reliability, 46(12), 2139-2148. 
                                                                                                                43. Szmidt, E., & Kacprzyk, J. (2001). Intuitionistic fuzzy sets in some medical applications. In Computational Intelligence. Theory and Applications (pp. 148-151). Springer Berlin Heidelberg. 
                                                                                                                44. Szmidt, E., Kacprzyk, J., & Bujnowski, P. (2014). How to measure the amount of knowledge conveyed by Atanassov’s intuitionistic fuzzy sets. Information Sciences, 257, 276-285. 
                                                                                                                45. Touran, A., Atgun, M., & Bhurisith, I., (2004). Analysis of the United States department of transportation prompt pay provisions. Journal of Construction Engineering and Management, 130(5), 719–725. 
                                                                                                                46. Ungureanu, D., & Vernic, R. (2014). On a fuzzy cash flow model with insurance applications. Decisions in Economics and Finance, 1-16. 
                                                                                                                47. Wang, Y. (2012). An Approach to Software Selection with Triangular Intuitionistic Fuzzy Information. International Journal of Advancements in Computing Technology, 4(2).
                                                                                                                48. Xu, Z., & Liao, H. (2014). Intuitionistic Fuzzy Analytic Hierarchy Process, IEEE Transactions on Fuzzy Systems, 22(4),749-761. 
                                                                                                                49. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. 
                                                                                                                50. Zimmermann, H. J. (2001).Fuzzy set theory—and its applications. Springer Science & Business Media.