基于云模型和多层权重求解的多粒度语言大群体决策方法

为了解决三阶段装配流水线调度问题,提出一种改进的离散型蝙蝠算法(DBA).针对所提问题的瓶颈期,提出下限理论,改进三阶段瓶颈期的下限公式,并引入调度模型生成初始种群,重新划分蝙蝠的捕食范围(HR),通过捕食行为、迁移行为的改进提高局部搜索能力,以有效提高离散蝙蝠算法的性能.改进K-means聚类算法,将具有最高相似性的蝙蝠进行分组,缩短计算时间,加快算法收敛速度.通过对不同规模实例的仿真实验与对比分析,对机器、产品和组的数量进行测试,验证了DBA的总体性能比其他算法更优;在算法的有效性和解的质量方面,通过对动态控制参数、DHR和精英策略的改进,有效地增强了算法的搜索能力.

Multi-granularity linguistic large group decision-making based on cloud model and multi-layer weight determination

In order to solve the three-stage assembly flowshop scheduling problem, this paper proposes an improved discrete bat algorithm (DBA). Aiming at the bottleneck period of the question, this paper proposes the lower limit theory and improves the lower limit formula of the three-stage bottleneck period. At the same time, a scheduling model is introduced to generate the initial population, and the bat''s hunting range (HR) is re-divided. Through the improvement of predation behavior and migration behavior, the local search ability of the algorithm is improved, and the performance of the discrete bat algorithm is effectively improved. The K-means clustering algorithm is improved to group the bats with the highest similarity, shortening the calculation time and speed up the algorithm convergence speed. Through simulation experiments and comparative analysis of examples of different scales, the number of machines, products and sets is tested, and the overall performance of the DBA is verified to be better than other algorithms; in terms of the effectiveness of the algorithm and the quality of the solution, the improvements for the dynamic control parameters, DHR and elite strategy effectively enhance the algorithm''s search capabilities.