Evaluation the profitability of dynamic investment projects by using ordered fuzzy numbers

محورهای موضوعی : Financial Mathematics

jamil Jalilian ¹ (Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
Reza Ehtesham Rasi ² (Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Mirfeiz Fallah Shams ³ (Department of Finance, Islamic Azad University , Central Tehran Branch, Tehran, Iran)

تاریخ دریافت : 1399/02/25 تاریخ پذیرش : 1399/08/25 تاریخ انتشار : 1402/06/10

کلید واژه: profitability, Fuzzy, investment projects, Capital budgeting, Dynamic,

چکیده مقاله :

The purpose of this paper is to provide a new approach to incorporating uncertainty into assessing the profitability of investment projects. In the real world, the capital budgeting problem is accompanied by uncertainty and risk associated dealing with imprecise data. The major contribution of this research is the development of a novel approach to evaluating the profitability of an investment project in uncertainty condition. At first, we presented a new discount method that can be used by investors when they wants to be able to make an investment decision. That is, we developed a new method to evaluate the profitability of investment projects by or-dered fuzzy net present value (OFNPV). In addition, ordered fuzzy numbers (OFN) are used to describe the dynamics of changes of the defined investment parameters in the assumed time horizon. By using ordered fuzzy numbers, we develop an effective tool for assessing the profitability of investment projects. This assessment tool not only enables decision-makers to decide under uncertainty conditions whether or not a given investment project should be carried out or rejected, but also facilitates selecting the most effective project, e.g. a project with the most expected probability of success.

منابع و مأخذ:

[1]          Tripathi, S., Application of Mathematics in Financial Management, (in en), Advances in Mathematical Finance and Applications,  2019, 4(2), P. 1-14. Doi:10.22034/amfa.2019.583576.1169.

[2] Awasthi, A., Omrani, H., A goal-oriented approach based on fuzzy axiomatic design for sustainable mobility project selection, International Journal of Systems Science: Operations and Logistics,  2019, 6(1), P. 86-98.

Doi: 10.1080/23302674.2018.1435834.

[3]          O. J. J. o. E. D., Gutierrez and Control, Devaluating projects and the investment–uncertainty relationship,  2007, 31(12), P.3881-3888. Doi: 10.1016/j.jedc.2006.12.013.

[4] Brigham, E. F., Houston, J. F., Fundamentals of financial management, 2012, Cengage Learning.

[5]          Kahraman, C., Kaya, İ., Fuzzy equivalent annual-worth analysis and applications, in Fuzzy Engineering Economics with Applications, 2008, Springer, P.71-81, Doi: 10.1007/978-3-540-70810-0_4.

[6]          Gajzler, M., Zima, K. J. I. J. o. C. E., Evaluation of planned construction projects using fuzzy logic,  2017, 15 (4), P.641-652. Doi: 10.1007/s40999-017-0177-8.

[7]          Kerzner, H., Project management: a systems approach to planning, scheduling, and controlling, 2017, John Wiley and Sons.

[8]          Kuchta, D., Fuzzy Capital Budgeting Fuzzy Sets and Systems,  2000, 111(3), Doi:10.1016/S0165-0114(98)00088-8.

[9]          Huang, X. J. C., Engineering, I., Mean-variance model for fuzzy capital budgeting,  2008, 55(1), P.34-47.

 Doi: 10.1016/j.cie.2007.11.015.

[10]        Kalantari, N., Mohammadi Pour, R., Seidi, M., Shiri, A., Azizkhani, M., Fuzzy Goal Programming Model to Rolling Performance Based Budgeting by Productivity Approach (Case Study: Gas Refiner-ies in Iran), (in en), Advances in Mathematical Finance and Applications,  2018, 3(3), P. 95-107. Doi: 10.22034/amfa.2018.544952.

[11]        Zamanian, M. R., Sadeh, E., Amini Sabegh, Z., Ehtesham Rasi, R., A Fuzzy Goal-Programming Model for Optimization of Sustainable Supply Chain by Focusing on the Environmental and Economic Costs and Revenue: A Case Study, (in en), Advances in Mathematical Finance and Applications,  2019, 4(1), P.103-123.

 Doi: 10.22034/amfa.2019.578990.1134.

[12]        Chwastyk, A., Kosiński, W. J. M. a., Fuzzy calculus with aplications,  2013, 41(1).

 Doi: 10.14708/ma.v41i1.380.

[13]        Chansa-ngavej, C., and C. A. J. I. J. o. P. E. Mount-Campbell, Decision criteria in capital budgeting under uncertainties: implications for future research,  1991, 23 (1-3), P. 25-35. Doi: 10.1016/0925-5273(91)90045-U.

[14]        Aliakbarpoor, Z., Izadikhah, M. Evaluation and ranking DMUs in the presence of both undesirable and ordinal factors in data envelopment analysis. Int. J. Autom. Comput. 2012, 9, P. 609–615. Doi: 10.1007/s11633-012-0686-5

 

[15]        Collan, M., Liu, S. J. I. M., D. Systems, Fuzzy logic and intelligent agents: towards the next step of capital budgeting decision support, Industrial Management & Data Systems,  2003, 103 (6).

Doi: 10.1108/02635570310479981.

[16]        M.-J. Wang and G.-S. J. T. E. E. Liang, Benefit/cost analysis using fuzzy concept,  1995, 40 (4), P. 359-376. Doi: 10.1080/00137919508903160.

[17]        Schneider, J., Kuchta, D. J. J. o. M.-V. L., Computing, S., Fuzzy Capital Budgeting for Projects Characterized by Step Type Fuzzy Intervals, Multiple Valued Log. Soft Comput,  2018, 30 P. 303-334.

[18]        J. J. J. F. s. Buckley and systems, The fuzzy mathematics of finance,  1987, 21 (3), P. 257-273. Doi: 0165-0114(87)90128-X.

[19]        Chen, S. J. T. E. E.,  An empirical examination of capital budgeting techniques: impact of investment types and firm characteristics,  1995, 40 (2), P. 145-170. Doi: 10.1080/00137919508903142.

[20]        Chiu,  C.-Y.,  Park, C. S. J. T. E. E., Capital budgeting decisions with fuzzy projects,  1998, 43(2), P. 125-150.

[21]        Kahraman, C., Kaya, İ. J. T., and Economy, E. D. o.,  Investment analyses using fuzzy probability concept,  2010, 16 (1), P. 43-57. Doi: 10.3846/tede.2010.03.

[22]        Tsao, C.-T. J. J. o. S., and Systems, M.,  The expectation-deviation net present value by fuzzy arithmetic for capital investments,  2010, 13 (2), P. 267-281. Doi: 10.1080/09720510.2010.10701469.

[23]        Gharanfoli, B., Valmohammadi, C.,  Identification and prioritization of construction projects investment risks using a hybrid fuzzy approach, Journal of Multi-Criteria Decision Analysis,  2019, 26 (3-4), P. 113-127. Doi: 10.1002/mcda.1661.

[24]        Kosiński, W. K., Kosiński, W., Kościeński, K. J. M., and Review, P. E., Ordered fuzzy numbers approach to an investment project evaluation, 2013, 4. Doi: 10.2478/mper-2013-0015.

[25]        Rudnik, K., Kacprzak, D. J. A. S. C., Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in a manufacturing system,  2017, 52, P. 1020-1041. Doi: 10.1016/j.asoc.2016.09.027.

[26]        Kosiński, W.,  Prokopowicz, P., Ślȩzak, D., Fuzzy reals with algebraic operations: Algorithmic approach, in Intelligent Information Systems 2002, 2002: Springer, P. 311-320, Doi: 10.1007/978-3-7908-1777-5_33.

[27]        Zhou, W., and Xu, Z. J. A. S. C, Hesitant fuzzy linguistic portfolio model with variable risk appetite and its application in the investment ratio calculation,  2019, 84,  P. 105719. Doi: 10.1016/j.asoc.2019.105719.

[28]        Mellichamp, D. A. J. C., Engineering, C., Profitability, risk, and investment in conceptual plant design: Optimizing key financial parameters rigorously using NPV%,  2019, 128 P. 450-467.

Doi: j.compchemeng.2019.04.016.

[29] Wu, Y., Xu, C., Ke, Y., Chen, K., Sun, X. J. E.,  An intuitionistic fuzzy multi-criteria framework for large-scale rooftop PV project portfolio selection: case study in Zhejiang, China,  2018, 143,  P. 295-309.

[30]        Zadeh, L. A. J. I. c., Fuzzy sets,  1965, 8(3), P. 338-353. Doi: 10.1016/S0019-9958(65)90241-X.

[31]        Dubois, D., and Prade, H. J. I. J. o. s. s., Operations on fuzzy numbers,  1978, 9(6), P. 613-626.

Doi: 10.1080/00207727808941724.

[32]        Sadollah, A., Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications, 2018, BoD–Books on Demand, Doi: 10.5772/intechopen.79552.

[33]        Radhika, C., and Parvathi, R., Defuzzification of intuitionistic fuzzy sets, Notes Intuitionistic Fuzzy Sets,  2016, 22(5), P.19-26.

[34]        Kosiński, W., On defuzzyfication of ordered fuzzy numbers, in International Conference on Artificial Intelligence and Soft Computing, 2004, P. 326-331, Springer, Doi: 10.1007/978-3-540-24844-6_46

[35] Kacprzak,     D., Input-Output Model Based on Ordered Fuzzy Numbers, in Theory and Applications of Ordered Fuzzy Numbers, 2017, Springer, Cham, P. 171-182, Doi: 10.1007/978-3-319-59614-3_9.