考虑运输代价的制造工作流调度算法

Manufacturing Workflow Scheduling Algorithm Considering Transportation Cost

在云制造环境下,因制造服务资源所在地域的差异性,多目标制造工作流调度不仅考虑制造服务所需时间、费用,还需考虑产品运输所需时间、费用,原有工作流调度算法无法有效优化运输代价.针对此问题,结合遗传算法全局搜索能力强与粒子群算法收敛速度快的特点,提出多目标混合遗传粒子群(MOGA-PSO)算法.仿真结果表明混合算法能够有效降低运输代价,使得工作流调度得到进一步优化,可适用于云制造环境.

In cloud manufacturing environment, for the geographical distribution of resources, the scheduling for manufacturing workflow with multiple goals should consider not only the cost of resources, but also the cost of product transportation. A hybrid algorithm is proposed to solve the problem which took the advantage of global optimization with genetic algorithm and fast convergence with particle swarm algorithm. Compared to the results of normal GA and PSO, simulation results show that the hybrid algorithm is an effective method for manufacturing work{low scheduling.