Research and Application of an Intelligent Production Scheduling APS System for Workshops Based on the Improved Teaching-Learning TLBO Algorithm

智能制造环境下，车间智能排产APS系统是企业数字化转型的关键技术之一，传统的排产方法存在效率低下、可扩展性差等问题，且难以应对日益复杂的生产环境。为此，本文提出了一种改进教与学TLBO优化算法，通过对TLBO算法引入局部搜索的方式改进算法的收敛性，结合车间生产的实际需求，使其能够处理多种约束条件和目标优化下的排产。通过仿真实验与其他传统算法(GA, PSO)进行对比，验证了改进TLBO算法在求解质量和计算效率方面的优势，并最终将该算法集成到APS系统中，实现了从数据输入、模型构建、优化求解到结果展示的全流程管理，有效地提升了生产效率和资源利用率。In the intelligent manufacturing environment, intelligent production scheduling APS system for workshops is one of the key technologies of enterprise digital transformation. The traditional production scheduling method has problems such as low efficiency and poor scalability, and it is difficult to cope with the increasingly complex production environment. Therefore, an improved TLBO optimization algorithm for teaching and learning is proposed. The convergence of the algorithm is improved by introducing local search method to TLBO algorithm. Combined with the actual demand of workshop production, it can handle production scheduling under various constraints and objective optimization. By comparing with other traditional algorithms (GA and PSO), simulation experiments verified the advantages of the improved TLBO algorithm in terms of solution quality and computational efficiency. Finally, the algorithm was integrated into APS system to realize the whole process management from data input, model construction, optimization and solution to result display, effectively improving production efficiency and resource utilization.