Розробка нечіткої стейкхолдерскої моделі оптимізації портфеля проектів рибопромислового підприємства з урахуванням ризиків

Автор(и)

  • Elena Likhosherst Владивостоцький державний університет економіки і сервісу вул. Гоголя, 41, м. Владивосток, Приморський край, Росія, 690014, Російська Федерація https://orcid.org/0000-0002-1715-196X
  • Lev Mazelis Владивостоцький державний університет економіки і сервісу вул. Гоголя, 41, м. Владивосток, Приморський край, Росія, 690014, Російська Федерація https://orcid.org/0000-0001-7346-3960
  • Konstantin Solodukhin Владивостоцький державний університет економіки і сервісу вул. Гоголя, 41, м. Владивосток, Приморський край, Росія, 690014, Російська Федерація https://orcid.org/0000-0003-3619-1219

DOI:

https://doi.org/10.15587/1729-4061.2019.176319

Ключові слова:

project portfolio optimization, accounting of stake¬holder requirements, utility function, fuzzy model

Анотація

The method for portfolio investment, allowing the formation of the optimal portfolio structure considering degrees of satisfaction of requirements of stakeholder groups, risks and uncertainty of external and internal environment, was proposed. The model that represents a fuzzy nonlinear programming problem was considered. The weighted average of project utility is used as objective function. The utilities of projects are multiplicative Cobb-Douglas type functions using, along with financial indicators, expert verbal evaluations of qualitative indi­cators of satisfaction of stakeholder requirements, converted into fuzzy numbers. Exponents in this function reflect the significance of stake­holders for the organization in terms of the existing resource sharing between a company and a stakeholder and the degree of mutual influ­ence. Quantitative accounting of risks is implemented based on the H. Markowitz approach and the scenario-based method. Uncertainty and lack of information for the indicator of economic efficiency of projects is modeled through the use of the fuzzy approach. Constraints in the model are also fuzzy. The transforming from a fuzzy optimization problem into a crisp problem is performed by assigning the satisfac­tion degrees for an objective function and the constraints. The choice of a certain satisfaction degree also makes it possible to some extent to take into account uncertainty, which, in turn, affects the composition of the portfolio. The solution to the model is found numerically using the proposed method, which allows, based on fuzzy utilities, finding fuzzy objective function and constraints, and transforming a fuzzy model into a crisp quadratic programming problem at specified satisfaction degrees. The example of the formation of an optimal portfolio of investment projects of a fishing industrial enterprise was explored.

Біографії авторів

Elena Likhosherst, Владивостоцький державний університет економіки і сервісу вул. Гоголя, 41, м. Владивосток, Приморський край, Росія, 690014

Аспірант

Кафедра математики і моделювання

Lev Mazelis, Владивостоцький державний університет економіки і сервісу вул. Гоголя, 41, м. Владивосток, Приморський край, Росія, 690014

Доктор економічних наук, завідувач кафедри

Кафедра математики та моделювання

Konstantin Solodukhin, Владивостоцький державний університет економіки і сервісу вул. Гоголя, 41, м. Владивосток, Приморський край, Росія, 690014

Доктор економічних наук, професор

Кафедра математики та моделювання

Посилання

  1. Gergert, D. V., Pronyushkin, D. V. (2013). Metody i modeli formirovaniya portfelya proektov. V sbornike: Sovershenstvovanie strategicheskogo upravleniya korporatsiyami i regional'naya innovatsionnaya politika. Materialy VI Rossiyskoy nauchno-prakticheskoy konferentsii s mezhdunarodnym uchastiem. Perm': Izd-vo Permskogo gosudarstvennogo natsional'nogo issledovatel'skogo universiteta, 50–55.
  2. Eskerod, P., Larsen, T. (2018). Advancing project stakeholder analysis by the concept “shadows of the context.” International Journal of Project Management, 36 (1), 161–169. doi: https://doi.org/10.1016/j.ijproman.2017.05.003
  3. Schwarzmüller, T., Brosi, P., Stelkens, V., Spörrle, M., Welpe, I. M. (2016). Investors’ reactions to companies’ stakeholder management: the crucial role of assumed costs and perceived sustainability. Business Research, 10 (1), 79–96. doi: https://doi.org/10.1007/s40685-016-0040-9
  4. Eskerod, P., Huemann, M., Ringhofer, C. (2015). Stakeholder Inclusiveness: Enriching Project Management with General Stakeholder Theory1. Project Management Journal, 46 (6), 42–53. doi: https://doi.org/10.1002/pmj.21546
  5. Voropaev, V. I., Gel'rud, Ya. D. (2012). Matematicheskie modeli proektnogo upravleniya dlya zainteresovannyh storon. Upravlenie proektami i programmami, 4, 258–269.
  6. Brunsson, N. (2007). The consequences of decision-making. Oxford University Press, 174.
  7. Winter, M., Szczepanek, T. (2008). Projects and programmes as value creation processes: A new perspective and some practical implications. International Journal of Project Management, 26 (1), 95–103. doi: https://doi.org/10.1016/j.ijproman.2007.08.015
  8. Ang, K. C. S., Killen, C. P. (2016). Multi-Stakeholder Perspectives of Value in Project Portfolios. In Proc. of EURAM, the 16th Annual conference of the European Academy of Management.
  9. Tamošiūnas, A. (2016). Managing stakeholders in complex investments projects. 9th International Scientific Conference “Business and Management 2016.” doi: https://doi.org/10.3846/bm.2016.41
  10. Khalilzadeh, M., Salehi, K. (2017). A multi-objective fuzzy project selection problem considering social responsibility and risk. Procedia Computer Science, 121, 646–655. doi: https://doi.org/10.1016/j.procs.2017.11.085
  11. Mazelis, L. S., Solodukhin, K. S. (2013). Multi-period models for optimizing an institution’s project portfolio inclusive of risks and corporate social responsibility. Middle-East Journal of Scientific Research, 17 (10), 1457–1461.
  12. Aras, A. C., Kaynak, O., Batyrshin, I. (2008). A comparison of fuzzy methods for modeling. 2008 34th Annual Conference of IEEE Industrial Electronics. doi: https://doi.org/10.1109/iecon.2008.4757927
  13. Carlsson, C., Fullér, R., Heikkilä, M., Majlender, P. (2007). A fuzzy approach to R&D project portfolio selection. International Journal of Approximate Reasoning, 44 (2), 93–105. doi: https://doi.org/10.1016/j.ijar.2006.07.003
  14. Novak, V., Perfilieva, I., Dvorak, A. (2016). Insight into Fuzzy Modeling. Wiley. doi: https://doi.org/10.1002/9781119193210
  15. Emrouznejad, A., Ho, W. (2017). Fuzzy analytic hierarchy process. New York: Chapman and Halll/CRC, 430. doi: https://doi.org/10.1201/9781315369884
  16. Anshin, V. (2015). Methodological aspects of measuring mutual effect of project portfolio and company’s goals. Scientific Research and Development. Russian Journal of Project Management, 4 (3), 3–8.
  17. Zhang, X., Hipel, K. W., Tan, Y. (2019). Project portfolio selection and scheduling under a fuzzy environment. Memetic Computing. doi: https://doi.org/10.1007/s12293-019-00282-5
  18. Zhou, X., Wang, J., Yang, X., Lev, B., Tu, Y., Wang, S. (2018). Portfolio selection under different attitudes in fuzzy environment. Information Sciences, 462, 278–289. doi: https://doi.org/10.1016/j.ins.2018.06.013
  19. Better, M., Glover, F. (2006). Selecting Project Portfolios by Optimizing Simulations. The Engineering Economist, 51 (2), 81–97. doi: https://doi.org/10.1080/00137910600695593
  20. Dixit, V., Tiwari, M. K. (2019). Project portfolio selection and scheduling optimization based on risk measure: a conditional value at risk approach. Annals of Operations Research. doi: https://doi.org/10.1007/s10479-019-03214-1
  21. Likhosherst, E. N., Mazelis, L. S., Chen, A. Ya. (2015). Selection of the optimal portfolio construction company taking into account the requests of stakeholdersin the formulation of multi-fuzzy. Vestnik Vladivostokskogo gosudarstvennogo universiteta ehkonomiki i servisa, 4, 27–40.
  22. Likhosherst, E., Mazelis, L., Gresko, A., Lavrenyuk, K. (2017). Fuzzy set model of project portfolio optimization inclusive for requirements of stakeholders. Journal of Applied Economic Sciences, 12 (5), 1263–1273.
  23. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7 (1), 77–91. doi: https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
  24. Soloduhin, K. S. (2009). Strategicheskoe upravlenie vuzom kak steykholder-kompaniey. Sankt-Peterburg: Izd-vo Politehn. un-ta, 290.
  25. Ptuskin, A. S. (2003). Investment projects ranking by risk level with the use of linguistic approach. Economics of Contemporary Russia, 3, 94–101.
  26. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1 (1), 3–28. doi: https://doi.org/10.1016/0165-0114(78)90029-5
  27. Demkin, I. V., Tsar'kov, I. N., Nikonov, I. M., An'shin, V. M. (2008). Primenenie teorii nechetkih mnozhestv k zadache formirovaniya portfelya proektov. Nauchnye issledovaniya i razrabotki. Problemy analiza riska, 5 (3), 8–21.

##submission.downloads##

Опубліковано

2019-08-20

Як цитувати

Likhosherst, E., Mazelis, L., & Solodukhin, K. (2019). Розробка нечіткої стейкхолдерскої моделі оптимізації портфеля проектів рибопромислового підприємства з урахуванням ризиків. Eastern-European Journal of Enterprise Technologies, 4(3 (100), 36–45. https://doi.org/10.15587/1729-4061.2019.176319

Номер

Розділ

Процеси управління