Abstract: To address the issue of easily falling into local optima and insufficient global exploration and local exploitation capabilities when solving the permutation flow-shop scheduling problem for minimizing makespan, an improved dung beetle optimization algorithm is proposed. Firstly, the algorithm employs an optimized Chebyshev chaotic map to initialize the population, aiming to enhance population diversity, expand the search area, and improve the overall optimization level. In the early stages of the algorithm, an adaptive convergence factor strategy is designed to achieve dynamic individual search, increase the algorithm's traversal capability, and enhance information exchange among dung beetle individuals, thereby making the algorithm's search space more comprehensive. In the later stages of iteration, a fused improved lens imaging inverse learning strategy and greedy selection strategy are utilized to balance global exploration and local exploitation, preventing the algorithm from getting trapped in local optima. Subsequently, the orthogonal experimental method was employed to determine the relevant parameters of the algorithm. Simulation experiments were conducted on the Car, Rec, and Taillard benchmark instances. The experimental data demonstrated that the proposed algorithm significantly outperforms other swarm intelligence optimization algorithms used for comparison. Finally, the effectiveness of the proposed algorithm was validated through its application to production scheduling in a steel pipe manufacturing enterprise.