The application of improved particle swarm optimization algorithm in dyeing scheduling problem

The textile dyeing industry faces intense competition, and with the increasing diversification and personalization of orders, the scheduling problem, especially in the dyeing process, has become critically important. This paper establishes a mathematical model for the dyeing scheduling problem and proposes an improved particle swarm optimization (PSO-C) algorithm to enhance scheduling efficiency. By introducing Tent chaotic mapping to increase population diversity and using the Lévy flight mechanism to optimize the step size, the algorithm avoids local optima. In addition, adaptive inertia weight adjustments and nonlinear variations in cognitive and social factors are incorporated to improve the convergence speed and accuracy of the algorithm. For local search, a unique greedy iterative algorithm is introduced, further enhancing search performance. Experimental results demonstrate that the proposed algorithm outperforms traditional methods, significantly improving both solution accuracy and efficiency for the dyeing scheduling problem.