A Novel Autonomous Scheduling Method for Cotton Spinning Workshop

To enhance the production scheduling issues in cotton spinning workshops, an improved genetic algorithm for production scheduling is proposed. The proposed algorithm improves the traditional genetic algorithm through enhanced encoding, decoding and crossover operation, constructs a multiple goals function model to address the scheduling issue in cotton spinning workshops, and aims to minimize the maximum processing time by using the processing time cost function to schedule production plans. Experimental results show that during the algorithm's solution process, when the number of repetitions grows, the population mean tends to stabilize, and the value of the population solution converges at 95 seconds. The change in the population mean also becomes stable, indicating that the genetic algorithm presented in this paper can effectively solve the production scheduling problems in the cotton spinning workshops of textile enterprises.