Affiliations: College of Mathematics and Computer Science, Yan’an University, Yan’an, China
Correspondence:
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Corresponding author. Jianren Shang, College of Mathematics and Computer Science, Yan’an University, Yan’an, China. E-mail: [email protected].
Abstract: In order to be able to efficiently carry out the management of workshop production scheduling, and improve the production efficiency and product quality, it is necessary and urgent to put forward a more flexible and efficient optimization algorithm. The combination of the genetic algorithm and particle swarm algorithm could give full play to each other’s characteristics, make up for deficiencies such as the low calculation speed of genetic algorithm and search scope limitations of optimum solution in the particle swarm optimization, and the hybrid particle swarm optimization algorithm was formed with fast computation speed and the reliable optimal solution. The hybrid algorithm was applied to the model of production scheduling, and the calculation steps and structure of the hybrid algorithm were defined. In order to verify the feasibility and effectiveness of the algorithm, simulation analysis was carried out by using Matlab. According to the analysis results, it can be seen that the hybrid algorithm applied to production scheduling is not only efficient but also flexible. The combination of genetic algorithm and particle swarm optimization algorithm to form a hybrid optimization algorithm has a certain reference value for the production scheduling similar algorithm optimization.
Keywords: Production scheduling, particle swarm optimization, hybrid, genetic algorithm
