考虑切换时间和成本的混装线排序优化

Sequencing Optimization of Mixed Model Assembly Line Regarding Switching Time and Cost

本文针对降低混装线企业中的生产成本问题,考虑工人闲置与超载时间内成本的差异,以最小化工作站内闲置与超载成本,最小化产品间的总切换时间为优化目标建立混装线投产排序数学模型。并设计了一种新改进的粒子群算法,算法中引入反向学习优化初始种群分布,从而提高了算法的寻优效率。为避免算法陷入局部收敛,将Baldwinian学习策略引入该算法中,增加了种群中粒子的多样性,提高了种群的全局搜索能力。结合具体实例的验证表明,该改进粒子群算法能有效地解决混装线投产排序问题。

Aiming at the problem of reducing production cost in the mixed-model assembly line, a sequencing model with optimized objective of minimizing simultaneously the total cost of idle-time and over-time as well as the total product set-up time is built while concerning different cost of workers in idle/overload time and shortening the production cycle of the products. Moreover, a new and improved particle swarm optimization（PSO） algorithm is proposed to solve the model. In the algorithm, the initial population distribution is introduced into the reverse learning optimization, which can improve the search efficiency of the algorithm. In order to avoid optimal solution plunging into a local convergence, the Baldwinian learning strategy was introduced into the algorithm to increase the diversity of particles in the population and improve the global searching ability of the population. The results of specific examples of verification show that the improved PSO algorithm can effectively solve the problem of mixed-model assembly lines sequencing problem.