Abstract
Resource Constrained Project Scheduling Problem (RCPSP) is a well known problem that is easy to describe but very difficult to solve, and therefore, it has attracted the attention of many researchers over the last few decades. In this context, heuristics are the only option when solving realistically-sized projects. In this paper we develop a steady-state genetic algorithm that uses a dynamic population and four decoding methods. These features allow the algorithm to adapt itself to the characteristics of the problem. Finally, its performance is compared against the best project scheduling methods published so far. The results show that the proposed scheduling method is one of the best scheduling techniques when compared with results reported in the literature.
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Cervantes, M., Lova, A., Tormos, P., Barber, F. (2008). A Dynamic Population Steady-State Genetic Algorithm for the Resource-Constrained Project Scheduling Problem. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_64
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DOI: https://doi.org/10.1007/978-3-540-69052-8_64
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