Multiobjective simulation optimisation in software project management

  • Authors:

  • Daniel Rodriguez;Mercedes Ruiz;José C. Riquelme;Rachel Harrison

  • Affiliations:

  • University of Alcala, Alcala de Henares, Spain;University of Cadiz, Cadiz, Spain;University of Seville, Seville, Spain;Oxford Brookes University, Oxford, United Kingdom

  • Venue:
  • Proceedings of the 13th annual conference on Genetic and evolutionary computation
  • Year:
  • 2011

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Abstract

Traditionally, simulation has been used by project managers in optimising decision making. However, current simulation packages only include simulation optimisation which considers a single objective (or multiple objectives combined into a single fitness function). This paper aims to describe an approach that consists of using multiobjective optimisation techniques via simulation in order to help software project managers find the best values for initial team size and schedule estimates for a given project so that cost, time and productivity are optimised. Using a System Dynamics (SD) simulation model of a software project, the sensitivity of the output variables regarding productivity, cost and schedule using different initial team size and schedule estimations is determined. The generated data is combined with a well-known multiobjective optimisation algorithm, NSGA-II, to find optimal solutions for the output variables. The NSGA-II algorithm was able to quickly converge to a set of optimal solutions composed of multiple and conflicting variables from a medium size software project simulation model. Multiobjective optimisation and SD simulation modeling are complementary techniques that can generate the Pareto front needed by project managers for decision making. Furthermore, visual representations of such solutions are intuitive and can help project managers in their decision making process.