Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Software project management with GAs
Information Sciences: an International Journal
A Task Allocation Optimizer for Software Construction
IEEE Software
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Design issues in a multiobjective cellular genetic algorithm
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Empirical findings on team size and productivity in software development
Journal of Systems and Software
A novel multiobjective formulation of the robust software project scheduling problem
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Robust solutions for the software project scheduling problem: a preliminary analysis
International Journal of Metaheuristics
Benchmarking CHC on a new application: the software project scheduling problem
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Not going to take this anymore: multi-objective overtime planning for software engineering projects
Proceedings of the 2013 International Conference on Software Engineering
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The Software Project Scheduling (SPS) problem relates to the decision of who does what during a software project lifetime. This problem has a capital importance for software companies. In the SPS problem, the total budget and human resources involved in software development must be optimally managed in order to end up with a successful project. Companies are mainly concerned with reducing both the duration and the cost of the projects, and these two goals are in conflict with each other. A multi-objective approach is therefore the natural way of facing the SPS problem. In this paper, a number of multi-objective metaheuristics have been used to address this problem. They have been thoroughly compared over a set of 36 publicly available instances that cover a wide range of different scenarios. The resulting project schedulings of the algorithms have been analyzed in order to show their relevant features. The algorithms used in this paper and the analysis performed may assist project managers in the difficult task of deciding who does what in a software project.