Outperforming genetic algorithm with a brute force approach based on activity-oriented petri nets

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.