Application of particle swarm optimisation algorithm incorporating frog-leaping algorithm in optimal scheduling for production management in manufacturing plant

The goal of management optimization scheduling in manufacturing plants is to improve machining efficiency and reduce costs, which is one of the research hotspots in the current era. The study proposed a particle swarm optimization algorithm incorporating the frog-leaping algorithm, which combined the grouping mechanism with the global search to improve the search speed of the algorithm, and also incorporated the mutation and crossover ideas of the genetic algorithm. To enhance the machining efficiency while minimizing time and resource requirements, two buffering mechanisms were used for the machining process in this algorithm. The algorithm ultimately achieved the optimal solution of 1170 around the 10th generation, according to experiments, which reduced the maximum machining time by 22%, the average production cycle time by 23%, the machine utilization rate reached 69%, and the percentage of the optimal relative error was almost less than 4%. Additionally, the algorithm’s average relative error fluctuation is less than that of the other algorithms, indicating that this algorithm is more stable. This result shows that the particle swarm optimization algorithm incorporating the frog hopping algorithm in this study has good practical value in optimizing the scheduling of production management in manufacturing workshops for improving the processing efficiency and reducing the cost, which is beneficial to the development of the manufacturing industry.