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A variable neighborhood search simheuristic for project portfolio selection under uncertainty

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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.

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Acknowledgements

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER. and the Erasmus+ programme (20161ES01KA108023465).

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Authors and Affiliations

  1. IN3 - Computer Science Department, Open University of Catalonia, Barcelona, Spain

    Javier Panadero & Angel A. Juan

  2. Economics and Business Department, Universitat Oberta de Catalunya, Barcelona, Spain

    Jana Doering & Angels Fito

  3. Economics and Finance Subject Group, Portsmouth Business School, University of Portsmouth, Portsmouth, UK

    Renatas Kizys

Authors
  1. Javier Panadero
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  2. Jana Doering
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  3. Renatas Kizys
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  4. Angel A. Juan
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  5. Angels Fito
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Correspondence to Javier Panadero.

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Panadero, J., Doering, J., Kizys, R. et al. A variable neighborhood search simheuristic for project portfolio selection under uncertainty. J Heuristics 26, 353–375 (2020). https://doi.org/10.1007/s10732-018-9367-z

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  • DOI: https://doi.org/10.1007/s10732-018-9367-z

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