遗传与粒子群算法在闭环混合系统运作中的应用

Optimal Operation Strategy for a Closed-loop Hybrid System with Genetic Algorithm and Particle Swarm Optimization

设计需求和再制造率不确定情况下的制造/再制造系统的最优回收及生产策略模型,并通过具体算例,采用遗传算法(GA)和粒子群算法(PSO)求解,对比验证模型的信度和效度。仿真结果表明,两种算法既可灵活获得多种情况下的系统最优运作策略,又能反映制造数量和回收品质量水平在不同再制造率下的变化规律,可为企业在多重不确定下降低总成本提供参考。

The optimal recycling and production strategy model based on random demand and yield is investigated in a closed-loop hybrid manufacturing/remanufacturing system. A numerical example is used to verify the validity and credibility of the model by adopting genetic algorithm （GA） and particle swarm optimization （PSO） algorithm. The results show that it is applicable for GA and PSO to solve the optimal operation strategy according to different situations and the variations of manufacturing quantity and the quality level of the returned items under the different remanufaeturing rate are presented. A reference could be provided for minimizing the total cost of the system in view of multiple uncertainties.