Scheduling optimization of silicon single crystal production process based on improved particle swarm algorithm

摘要

Aiming at the hidden safety hazards of the factory in the process of growing silicon single crystals in multiple furnaces, This paper considers the factory's maximum power load requirements, and establishes a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time. Aiming at the above model, this paper uses an improved particle swarm optimization algorithm (GPSO) to solve the problem. The improved particle swarm algorithm retains the individual and global optimal values of the particle swarm algorithm, and introduces the crossover and mutation operations from the genetic algorithm to increase the population diversity to avoid the results from falling into the local optimum. Solving problems of different scales shows the feasibility and effectiveness of the GPSO algorithm in solving the scheduling problem of silicon single crystal production process.