Scaling up a Project Portfolio Selection Technique by using Multiobjective Genetic Optimization

Authors

  • Márcio de Oliveira Barros PPGI-UNIRIO
  • Hélio Costa COPPE/UFRJ
  • Fábio Vitorino Figueiredo PPGI-UNIRIO
  • Ana Regina Cavalcanti Rocha COPPE/UFRJ

Abstract

This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requires analyzing all combinations of the candidate projects, which turns to be unfeasible when more than a few alternatives are available. We have used a multiobjective genetic algorithm to partially explore the search space of project combinations and select the most effective ones. We present an experimental study based on four real-world project selection problems that compares the results found by the genetic algorithm to those yielded by a non-systematic search procedure (random search). A second experimental study evaluates the best parameter settings to perform the heuristic search. Experimental results show evidence that the project selection technique can be used in large-scale scenarios and that the genetic algorithm presents better results than simpler search strategies.

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Published

2014-11-17

How to Cite

Barros, M. de O., Costa, H., Figueiredo, F. V., & Rocha, A. R. C. (2014). Scaling up a Project Portfolio Selection Technique by using Multiobjective Genetic Optimization. ISys - Brazilian Journal of Information Systems, 7(4), 60–74. Retrieved from https://seer.unirio.br/isys/article/view/2221

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Section

REGULAR ARTICLES