Abstract
Scheduling problems are NP-hard, thus have few alternative methods for obtaining solutions. Genetic algorithms have been used to solve scheduling problems; however, the application of genetic algorithms are too expectant, as the steps involved in a genetic algorithm, especially the reproduction step and the selection step, are often time-consuming and computationally expensive. This is because the newly reproduced chromosomes are often redundant or invalid. This paper proposes a brute-force approach for solving scheduling problems, as an alternative to genetic algorithm; the proposed approach is based on Activity-oriented Petri nets (AOPN) and is computationally simple; in addition, the proposed approach also provides the optimal solution as it scans the whole workspace, whereas genetic algorithm does not guarantee optimal solution.
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Davidrajuh, R. (2017). Outperforming Genetic Algorithm with a Brute Force Approach Based on Activity-Oriented Petri Nets. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_44
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DOI: https://doi.org/10.1007/978-3-319-47364-2_44
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