1. Barkalov S.A., Voropaev V.I., Sekletova G.I. Matematicheskie osnovy upravleniya proektami [Mathematical Foundations of Project Management]. Moscow, Vysshaya shkola Publ., 2005. 423 p.
2. Biryuk S. Metod Monte-Karlo. Modelirovanie po metodu Monte-Karlo. Analiz riskov s ispol´zovaniem metoda Monte-Karlo [Monte Carlo method. Simulation Monte Carlo. The risk analysis using Monte Carlo method]. Available at: http://spiderproject.com.ua/company/news/7262/
3. Bukhvalov A.V. Real´ny li real´nye optsiony [Is it possible to real options]. Rossiyskiy zhurnal menedzhmenta [Russian Management Journal]. 2006, I. 4(3), pp. 77-84.
4. Bukhvalov A.V. Real´nye optsiony v menedzhmente: vvedenie v problemu [Real Options in Management: introduction to the problem]. Rossiyskiy zhurnal menedzhmenta [Russian Management Journal]. 2004, I. 1, pp. 3-32.
5. Bukhvalov A.V. Real´nye optsiony v menedzhmente: klassifikatsiya i prilozheniya [Real Options in Management: Classification and Applications]. Rossiyskiy zhurnal menedzhmenta [Russian Management Journal]. 2004, I. 2, pp. 27-56.
6. Volkov I., Gracheva M. Veroyatnostnye metody analiza riskov [Probabilistic methods of risk analysis]. Available at: http://www.cfin.ru/finanalysis/monte_carlo2.shtml
7. Voropaev V.I., Gel´rud Ya.D. Obobshchennye stokhasticheskie setevye modeli dlya upravleniya kompleksnymi proektami (chast´ 1) [Generalized stochastic network models for complex project management (part 1)]. Upravlenie proektami i programmami [Project and Program Management.]. 2008, I. 1, pp. 2-13.
8. Voropaev V.I., Gel´rud Ya.D. Obobshchennye stokhasticheskie setevye modeli dlya upravleniya kompleksnymi proektami (chast´ 2) [Generalized stochastic network models for complex project management (Part 2)]. Upravlenie proektami i programmami [Project and Program Management.]. 2008, I. 2, pp. 92-104.
9. Voropaev V.I., Gel´rud Ya.D., Golenko-Ginzburg D.I., Ben-Yar A. Prinyatie resheniy v upravlyaemykh tsiklicheskikh al´ternativnykh setevykh modelyakh dlya proektov s determinirovannymi vetvleniyami [Decision-making under controlled cyclic alternative network models for projects with deterministic branching]. Upravlenie proektami i programmami [Project and Program Management]. 2010, I. 1, pp. 4-14.
10. Voropaev V., Gel´rud Ya., Klimenko O. Funktsional´nye modeli upravleniya proektnoy deyatel´nost´yu dlya raznykh zainteresovannykh storon [Functional project management model for different stakeholders]. Upravlenie proektami i programmami [Project and Program Management]. 2014, I. 4, pp. 266-278.
11. Golenko D.I. Statisticheskie metody setevogo planirovaniya i upravleniya [Statistical methods for network planning and management]. Moscow, Nauka Publ., 1969. 400 p.
12. Kosukhina M.A., Brusakova I.A., Barykin S.E. Metodika otsenki effektivnosti upravleniya korporativnymi finansami v usloviyakh neopredelennosti [Methods of assessing the effectiveness of corporate finance in the conditions of uncertainty]. Audit i finansovyy analiz [Audit and financial analysis]. 2012, I. 4, pp. 68-77.
13. Kryukov S.V. Bayesovy seti kak instrument modelirovaniya neopredelennosti pri prinyatii investitsionnykh resheniy [Bayesian networks as a tool for modeling uncertainty in investment decisions]. Ekonomicheskiy vestnik Rostovskogo gosudarstvennogo universiteta [Economic Gazette Rostov State University]. 2007, I. 5(1), pp. 106-111.
14. Kryukov S.V. Vybor metodov i modeley otsenki effektivnosti investitsionnykh proektov v usloviyakh neopredelennosti [Selection of methods and models for assessing the effectiveness of investment projects under conditions of uncertainty]. Ekonomicheskiy vestnik Rostovskogo gosudarstvennogo universiteta [Economic Gazette Rostov State University]. 2008, I. 6(3), pp. 107-113.
15. Lukashov A.V. Metod Monte-Karlo dlya finansovykh analitikov: kratkiy putevoditel´ [Monte Carlo method for financial analysts: a brief guide]. Upravlenie korporativnymi finansami [Management of corporate finances]. 2007, I. 01(19), pp. 22-39.
16. Nalivkin D.V. Ispol´zovanie posledovatel´nykh metodov Monte-Karlo dlya otsenivaniya riskov na finansovykh rynkakh [Using sequential Monte Carlo methods for the evaluation of risks in the financial markets]. Upravlenie bol´shimi sistemami: sbornik trudov [Managing large systems: Proceedings]. 2008, I. 21. Available at: http://cyberleninka.ru/article/n/ispolzovanie-posledovatelnyh-metodov-monte-karlo-dlyaotsenivaniya-riskov-na-finansovyh-rynkah
17. Privalov A.I. Matematicheskie modeli upravleniya proektami v reshenii sistemnykh problem ekonomiki [Mathematical model of project management in addressing systemic problems of economy]. Ekonomicheskie nauki [Economic sciences]. 2009, I. 1(50), pp. 337-340.
18. Saati T.L. Prinyatie resheniy. Metod analiza ierarkhiy [Making decisions. Analytic Hierarchy Process]. Moscow, Radio i svyaz´ Publ., 1989. 316 p.
19. Titarenko B.P. Upravlenie riskami v ramkakh sistemnoy modeli proektno-orientirovannogo upravleniya proektami [Risk management within the framework of a systemic model of project-oriented project management]. Upravlenie proektami i programmami [Project and Program Management]. 2006, I. 1, pp. 76-89.
20. Fel´ker R. Ispol´zovanie teorii igr v praktike upravleniya [Using game theory to practice management]. Problemy teorii i praktiki upravleniya [Problems of the theory and practice of management]. 1999, I. 5, pp. 86-93.
21. Kharshan´i Dzh., Zel´ten R. Obshchaya teoriya vybora ravnovesiya v igrakh [The general theory of equilibrium selection in games]. St. Petersburg, Ekonomicheskaya shkola Publ., 2001. 424 S.
22. Angling M. (1988). Resource planning and control in a multiproject environment. International Journal of Project Management, Vol. 6(4), pp. 197-201.
23. Beikci U., Bilge Ü, Ulusoy G. (2015). Multi-mode resource constrained multi-project scheduling and resource portfolio problem. European Journal of Operational Research, Vol. 240, pp. 22-31.
24. Brauer D.C., Naadimuthu G., LeeE.S. (1987).Effective program planning for multiple projects under limited resources. Mathematical Modelling, Vol. 9(7), pp. 547-552.
25. Chen V. (1994). A 0-1 goal programming model for scheduling multiple maintenance projects at copper mine. European Journal of Operational Research, Vol. 76(1), pp. 176-191.
26. Coyne K.P., Subramaniam S. (1996) Bringing discipline to strategy. The McKinsey Quarterly. Vol 4, pp. 14-25
27. Gonçalves J.F., Mendes J.J.M., Resende M.G.C. (2008). A genetic algorithm for the resource constrained multiproject scheduling problem. European Journal of Operational Research, Vol. 189(3), pp. 1171-1190.
28. Kannan A., Shanbhag U., Kim H. (2011). Strategic behavior in power markets under uncertainty. Energy Systems, Vol. 2, pp. 115-141.
29. Kim K., Yun Y., Yoon J., Gen M., Yamazaki G. (2005). Hybrid genetic algorithm with adaptive abilities for resourceconstrained multiple project scheduling. Computers in Industry, Vol. 56(2), pp. 143-160.
30. Krüger D., Scholl A. (2009). A heuristic solution framework for the resource constrained (multi-) project scheduling problem with sequence-dependent transfer times. European Journal of Operational Research, Vol. 197, pp. 492-508.
31. Liu M., Shan M., Wu J. (2014). Multiple R&D Projects Scheduling Optimization with Improved Particle Swarm Algorithm. The Scientific World Journal, pp. 1-7.
32. Liu S., Wang C.-J. (2010). Profit Optimization for Multiproject Scheduling Problems Considering Cash Flow. Journal of Construction Engineering and Management, Vol. 136(12), pp. 1268-1278.
33. Lova A., Maroto C., Tormos P. (2000). A multicriteria heuristic method to improve resource allocation in multiproject scheduling. European Journal of Operational Research, Vol. 127(2), pp. 408-424.
34. Marreco J., Carpio L. (2006). Flexibility valuation in the Brazilian power system: A real options approach, Energy Policy, Vol. 34, pp. 3749-3756.
35. Maylor H., Brady T., Cooke-Davies T., Hodgson D. (2006). From projectification to programmification. International Journal of Project Management, Vol. 24, pp. 663-674.
36. Mills E., Kromer S., Weiss G., Mathew P. (2006). From volatility to value: analysing and managing financial and performance risk in energy savings projects. Energy Policy, Vol. 34, pp. 188-199.
37. Mueller J. (2013). Estimating Arizona residents’ willingness to pay to invest in research and development in solar energy. Energy Policy, Vol. 53, pp. 462-476.
38. Pritsker A., Watters L., Wolfe P. (1969). Multiproject scheduling with limited resources: a zero-one programming approach. Management Science, Vol. 16 (1), pp. 93-108.
39. Siddiquia A., Marnay C., Wiser R. (2007). Real options valuation of US federal renewable nergy research, development, demonstration, and deployment, Energy Policy, Vol. 35, pp. 265-279.
40. Singh A. (2014). Resource Constrained Multi-Project Scheduling with Priority Rules and Analytic Hierarchy Process. Procedia Engineering, Vol. 69, pp. 725-734.
41. Speranza M.G., VercellisS. (1993). Hierarchical models for multi-project planning and scheduling. European Journal of Operational Research, Vol. 64, pp. 312-325.
42. Spinney P.J., Watkins G.C. (1996). Monte Carlo simulation techniques and electric utility resource decisions. Energy Policy, Vol. 24(2), pp. 155-163.
43. Tavares L.V. (1987). Optimal resource profiles for program scheduling. European Journal of Operational Research, Vol. 29(1), pp. 83-90.
44. Trypia M. (1980). Cost minimization of m simultaneous projects that require the same scarce resource. European Journal of Operational Research, Vol. 5(4), pp. 235-238.
45. Vásconez V., Giraud G., McIsaac F., Pham N.-S. (2015). The effects of oil price shocks in a new-Keynesian framework with capital accumulation.Energy Policy, Vol. 86, pp. 844-854.
46. Vithayasrichareon P., MacGillI. (2012). A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries.Energy Policy, Vol. 41, pp. 374-392.
47. Wiley V., Deckro R., Jackson J. (1998). Optimization analysis for design and planning of multi-project programs. European Journal of Operational Research, Vol. 107, pp. 492-506.
48. Zapata J., Hodge B., Reklaitis G. (2008). The multimode resource constrained multiproject scheduling problem: Alternative formulations. AIChEJournal, Vol. 54 (8), pp. 2101-2119.
