The software project scheduling problem: A scalability analysis of multi-objective metaheuristics

Applied Soft Computing 15, 136-148Computer aided techniques for scheduling software projects are a crucial step in the software development process within the highly competitive software industry. The Software Project Scheduling (SPS) problem relates to the decision of who does what during a software project lifetime, thus involving mainly both people-intensive activities and human resources. Two major conflicting goals arise when scheduling a software project: reducing both its cost and duration. A multi-objective approach is therefore the natural way of facing the SPS problem. As companies are getting involved in larger and larger software projects, there is an actual need of algorithms that are able to deal with the tremendous search spaces imposed. In this paper, we analyze the scalability of eight multi-objective algorithms when they are applied to the SPS problem using instances of increasing size. The algorithms are classical algorithms from the literature (NSGA-II, PAES, and SPEA2) and recent proposals (DEPT, MOCell, MOABC, MO-FA, and GDE3). From the experimentation conducted, the results suggest that PAES is the algorithm with the best scalability features.Spanish Ministry of Science and Innovation and ERDF (European Regional Development Fund) under contract TIN2008-06491-C04 (M* project). Spanish Ministry of Economy and Competitiveness and the ERDF under contracts TIN2012-30685 (BIO project) and TIN2011-28194 (roadME project). Fundación Valhondo, for the economic support offered to David L. González-Álvarez