Multi-objective scheduling problems are inherently complex due to the need to balance competing objectives, such as minimizing the total weighted completion time, reducing the number of delayed jobs, and minimizing the maximum weighted delay.To address these challenges, this article ilootpaperie introduces the meerkat clan algorithm (MCA), inspired by the dynamic, cooperative, and adaptive behaviors of meerkats, which enhances the exploration and exploitation of solution spaces.The MCA is further integrated with the traditional branch-and-bound (BAB) method, utilizing it as an upper bound to significantly improve the accuracy and efficiency of the solutions.Comprehensive computational experiments were conducted to evaluate the MCA’s performance against state-of-the-art algorithms, including the bald eagle search optimization algorithm (BESOA) and the standalone BAB method.The MCA demonstrated superior scalability and efficiency, effectively solving problems involving up to n = 30,000 jobs, whereas the BESOA was limited to handling instances with n = 1,000 jobs.
Additionally, the integration of MCA with the BAB method achieved exceptional precision and efficiency for smaller problem instances, handling up to n = 13 jobs effectively.The results underscore the MCA algorithm’s potential as a robust solution for multi-objective scheduling problems, combining speed and accuracy to outperform traditional methods.Moreover, the hybrid approach of integrating MCA with BAB provides turbo air m3f24-1-n a flexible and versatile framework capable of addressing a wide range of scheduling scenarios, from small-scale to large-scale applications.These findings position the MCA as a transformative tool for solving complex scheduling problems in both theoretical and practical domains.