改进PSO-GA算法求解混合流水车间调度问题

Improved PSO-GA Algorithm for Hybrid Flow Shop Scheduling Problem

为解决混合流水车间排产优化中企业较为关注的最大加工时间最小问题及设备负载均衡问题,引入综合评价指标函数,并建立了混合整数规划数学模型.改进了基本遗传-粒子群算法,充分利用了粒子群算法搜索速度快的优点及遗传算法搜索范围广的优点,且在算法寻优过程中引入随遗传与迁移代数自适应调整参数的策略,相较于原算法,改进后的算法有更强的跳出局部最优、保持活力的能力.基于汽车生产线数据对遗传算法、粒子群算法及改进遗传-粒子群算法进行了仿真比较,结果表明:改进后的算法在综合评价指标函数与所需收敛代数上均优于普通遗传算法与粒子群算法.

In order to solve the problem of minimum maximum processing time and equipment load balance in the scheduling optimization of hybrid flow shop,a mathematical model of mixed integer programming was established by introducing the comprehensive evaluation index function.The basic genetic-particle swarm optimization algorithm was improved,and the advantages of fast searching speed of particle swarm optimization algorithm and wide searching range of genetic algorithm were fully utilized.In the process of algorithm optimization,the strategy of adaptive parameter adjustment with heredity and migration algebra was introduced.Compared with the original algorithm,the improved algorithm has stronger ability to jump out of local optimum and keep vitality.Based on the data of automobile production line,the genetic algorithm,particle swarm optimization algorithm and improved genetic-particle swarm optimization algorithm were simulated and compared.The results show that the improved algorithm is superior to general genetic algorithm and particle swarm optimization algorithm in comprehensive evaluation index function and convergence algebra.