摘要
针对传统调度方法在求解柔性作业车间调度问题时存在的结构复杂、参数多变等问题,提出了一种运算效率和求解性能均较好的教与学算法。首先,采用全局选择、局部选择和随机选择策略,提高了种群的多样性,避免陷入局部最优;其次,使用一种自适应的教学因子改善了教与学算法容易引起的早熟收敛问题;然后,基于教学算法“学”阶段的自学与邻域搜索策略相结合,提升了算法的局部搜索效能,改善了算法的“早熟”问题;最后对Kacem和MK系列算例进行了模拟实验并与其他算法进行比较,实验结果表明:改进的算法在求解柔性车间调度问题上较其他几种算法而言具有较强优势。
To address the problems of complex structure and varying parameters in traditional scheduling methods for solving flexible job shop scheduling problems,a teaching and learning algorithm with good computational efficiency and solution performance is proposed.Firstly,adopting global selection,local selection,and random selection strategies enhances the diversity of the population and avoids falling into local optima.Secondly,an adaptive teaching factor is used to improve the problem of premature convergence that is easily caused by the teaching and learning algorithm.Then,based on the self-learning and neighborhood search strategies in the"learning"stage of the teaching algorithm,the local search efficiency of the algorithm is improved and the problem of"premature convergence"is alleviated.Finally,simulation experiments are conducted on Kacem and MK series examples and compared with other algorithms.The experimental results show that the improved algorithm has a strong advantage over other algorithms in solving flexible workshop scheduling problems.
作者
姜润菲
陶泽
JIANG Runfei;TAO Ze(School of Mechanical Engineering,Shenyang Ligong University,Shenyang 110159,China)
出处
《机械工程与自动化》
2025年第1期16-19,共4页
Mechanical Engineering & Automation
基金
辽宁省应用基础研究计划项目(2022JH2/101300254)。
关键词
柔性车间调度
自适应教学因子
教与学算法
邻域搜索策略
flexible job shop scheduling
adaptive teaching factors
teaching and learning algorithms
neighborhood search strategy
