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  1. 2 Operations Research and Decisions

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  1. 2 RĘBIASZ B.

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  1. 1 2019

  2. 1 2016

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New method of selecting efficient project portfolios in the presence of hybrid uncertainty

100%
RĘBIASZ B.
Operations Research and Decisions
|
2016
|
vol. 26
|
issue 4
65-90
XX
A new methods of selecting efficient project portfolios in the presence of hybrid uncertainty has been presented. Pareto optimal solutions have been defined by an algorithm for generating project portfolios. The method presented allows us to select efficient project portfolios taking into account statistical and economic dependencies between projects when some of the parameters used in the calculation of effectiveness can be expressed in the form of an interactive possibility distribution and some in the form of a probability distribution. The procedure for processing such hybrid data combines stochastic simulation with nonlinear programming. The interaction between data are modeled by correlation matrices and the interval regression. Economic dependences are taken into account by the equations balancing the production capacity of the company. The practical example presented indicates that an interaction between projects has a significant impact on the results of calculations.
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The valuation of real options in a hybrid environment

88%
RĘBIASZ B.
Operations Research and Decisions
|
2019
|
vol. 29
|
issue 1
97-119
EN
The aim of this paper is to present the possibilities and purposefulness of the application of fuzzy set theory to the valuation of real options. Owing to temporal fluctuations in the market, some input parameters in a model of a real option cannot always be expressed in a precise sense. Therefore, it is natural to consider them as a fuzzy numbers. Such an approach allows us to keep more information about the possible value of real options. A hybrid (fuzzy-stochastic) model for valuing a switch option is presented. Under these assumptions, the value of a switch option will be a fuzzy random set. This article assesses the incremental benefit of product switch options in steel plant projects. Such options are valued by Monte Carlo simulation and modelling the prices of and demand for steel products using fuzzy geometric Brownian motion. Finally, the value of a product switch option is defined by the upper and lower probability distribution function
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