Outperforming Genetic Algorithm with a Brute Force Approach Based on Activity-Oriented Petri Nets

• Reggie Davidrajuh ^[1]
  1. [1] University of Stavanger

     University of Stavanger

     Noruega

     
• Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay, José Manuel López Guede, Oier Etxaniz, Álvaro Herrero Cosío, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2017, ISBN 978-3-319-47364-2, págs. 454-463
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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 pro-posed 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.