A K-means-Teaching Learning based optimization algorithm for parallel machine scheduling problem - Li, Yibing; Liu, Jie; Wang, Lei; Liu, Jinfu; Tang, Hongtao; Guo, Jun; Xu, Wenxiang (Applied soft computing, 2024)
With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solution result. However, data mining has a good performance in solving large-scale scheduling problems. A data mining method was proposed for industrial big data to solve the problem of large-scale parallel machines scheduling. This methodology can obtain an effective initial solution for single-operation parallel machine scheduling problem by exploring the effective information in the historical scheduling data. Based on historical customer orders, the offline learning was used to continuously generate simulated data for learning, which makes up for the shortcomings of insufficient data. A TLBO framework (teaching-learning-based optimization) hybrid K-means algorithm was redesigned to enhance the accuracy of offline learning and the efficiency of data searching. In the online operation part, according to the optimal solutions for high-similarity manufacturing orders are the approximate solutions, the new customer order will be quickly matched with the most similar manufacturing order through similarity calculation, and then and then local search is performed. Finally, the globally optimal solution is obtained after screening. Experimental results show that the hybrid teaching–learning methodology can solve the large-scale parallel machines scheduling problem with a better learning performance and computational efficiency. ●A data-driven hybrid learning methodology is proposed, which consists of the offline learning part and the online scheduling part.●A KTLBO algorithm is proposed to learn the optimal scheduling solutions for sample manufacturing orders and historical customer orders in offline learning part.●It can be considered that the optimal solutions for high-similarity manufacturing orders are also approximate in the solution space, an improved K-means algorithm is applied to cluster similar manufacturing orders.●A local search algorithm is proposed to find the global optimal solution of current customer order around the cluster center in the online scheduling part.