Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
A Review of Surveys on Software Effort Estimation
ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
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
Factors affecting duration and effort estimation errors in software development projects
Information and Software Technology
Cellular Genetic Algorithms
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
Software—Practice & Experience
Using multi-objective metaheuristics to solve the software project scheduling problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
IEEE Transactions on Evolutionary Computation
A novel multiobjective formulation of the robust software project scheduling problem
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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The software project scheduling 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 software project scheduling problem, the total budget and human resources involved in software development must be optimally managed in order to end up with a successful project. Two are the main objectives identified in this problem: minimising the project cost and minimising its makespan. However, some of the parameters of the problem are subject to unforeseen changes. In particular, the cost of the tasks of a software project is one of the most varying parameters, since it is related to estimations of the productivity of employees. In this paper, we modify the formulation of the original bi-objective problem to add two new objectives that account for the robustness of the solutions to changes in the problem parameters. We address 36 instances of this optimisation problem using four state-of-the-art metaheuristic algorithms and compare the solutions with those of the original non-robust bi-objective problem.