Abstract: A multi-strategy adaptive dung beetle optimization algorithm (MSA-DBO) is proposed for the flexible job-shop scheduling problem (FJSP) with the objective of minimizing makespan. Firstly, the Logistic-tent chaotic mapping and the G-L-R strategy are used to improve population initialization, ensuring a more uniform distribution in the population and enhancing the quality of the initial solution. Secondly, a tournament selection strategy is applied to form a superior population after evaluating the fitness of each dung beetle, in order to accelerate convergence. Thirdly, the Golden Sine strategy is used to improve the position update formula for dung beetles when encountering obstacles, helping to avoid local optima. Finally, after updating the position of the dung beetles, an elite random reverse learning strategy and a critical path adaptive rescheduling strategy are added to enhance communication and global optimization capabilities among dung beetle individuals in the population. Simulation experiments and comparisons are carried out using Brandimarte's benchmark instances and real-world cases, and the results showed that the improved strategies of the MSA-DBO algorithm are effective, significantly enhancing solution accuracy and algorithm performance.