A variable neighborhood search simheuristic for project portfolio selection under uncertainty

Javier Panadero (), Jana Doering (), Renatas Kizys (), Angel Juan and Angels Fito ()
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Javier Panadero: Open University of Catalonia
Jana Doering: Universitat Oberta de Catalunya
Renatas Kizys: University of Portsmouth
Angels Fito: Universitat Oberta de Catalunya

Journal of Heuristics, 2020, vol. 26, issue 3, No 3, 353-375

Abstract: Abstract With limited financial resources, decision-makers in firms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash flows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases.

Keywords: Project portfolio selection; Stochastic optimization; Net present value; Variable neighborhood search; Simheuristics (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10732-018-9367-z

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