基于GA-PSO算法的烧结料场原料库存量优化  

Raw material inventory optimization algorithm for sinter material plant based on GA-PSO

作　　者：蔡雁[1,2] 钟茜怡[3] 吴敏[1,2] 周晋妮[1,2] 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410083 [2]先进控制与智能自动化湖南省工程实验室,湖南长沙410083 [3]西安电子科技大学电子工程学院,陕西西安710071

出　　处：《化工学报》2012年第9期2824-2830,共7页CIESC Journal

基　　金：国家高技术研究发展计划项目(2012AA040307)~~

摘　　要：原料采购库存成本的约束是钢铁企业流动资金的制约瓶颈,针对钢铁企业烧结料场铁矿粉原料采购与消耗特点,以企业原料库存费用最小为目标建立了烧结料场铁矿粉原料库存量优化模型,提出一种基于遗传-粒子群算法的烧结料场铁矿粉库存量优化方法。同时,应用某钢铁企业360m2烧结生产线的综合原料场实际生产数据进行仿真验证,结果表明,该模型可以反映该钢铁企业综合料场铁矿粉库存量的实际情况,采用的优化方法可以得到模型的最优解,为钢铁企业采购计划的制定提供决策支持。The raw material purchasing and storage cost are the capital bottleneck of iron and steel enterprises.According to the purchasing and consuming characteristics of sinter material plant,the iron ore raw material inventory optimization model committed to minimum cost was established.Furthermore,an optimation method which combine the particle swam optimation（PSO）and genetic algorithm（GA）was developed to solve the question.To prove the effectiveness of this method,it was validated by the actual running data of a 360 m2 sintering production line in an iron and steel enterprise.The simulation results showed that the model could feedback the real condition of raw material inventory in sinter plant,and the optimal solution could be obtained by the GA-PSO method,which could give a reliable support for the material purchasing decision.

关 键 词：烧结料场 库存优化 粒子群优化 遗传-粒子群算法 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]