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.
Highlights
Project Portfolio Management has gained attention in recent years, as organizations became increasingly project, program, and portfolio-oriented [3]
We present a multiobjective heuristic optimization approach to support the application of the technique proposed by Costa et al [2] in large-scale scenarios on regard of the number of candidate projects available to comprise the portfolio
Our primary contributions are as follows: (i) a multiobjective heuristic optimization approach to support the application of the project portfolio selection technique in scenarios with a large number of candidate projects; and (ii) experimental studies to determine the most appropriate parameter settings for the proposed multiobjective heuristic search and to compare it with a simpler, non-systematic search procedure
Summary
Project Portfolio Management has gained attention in recent years, as organizations became increasingly project-, program-, and portfolio-oriented [3]. We present a multiobjective heuristic optimization approach to support the application of the technique proposed by Costa et al [2] in large-scale scenarios on regard of the number of candidate projects available to comprise the portfolio. Our primary contributions are as follows: (i) a multiobjective heuristic optimization approach to support the application of the project portfolio selection technique in scenarios with a large number of candidate projects; and (ii) experimental studies to determine the most appropriate parameter settings for the proposed multiobjective heuristic search and to compare it with a simpler, non-systematic search procedure Besides this introduction, this paper is organized in six sections.
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