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基于智能算法的啤酒灌装生产线设备选型研究
引用本文:贾瑞,李光.基于智能算法的啤酒灌装生产线设备选型研究[J].包装工程,2019,40(9):135-141.
作者姓名:贾瑞  李光
作者单位:天津科技大学,天津,300222;天津科技大学,天津,300222
基金项目:天津市自然科学基金(17JCTPJC54900)
摘    要:目的以啤酒灌装生产线为研究对象,基于粒子群智能算法,实现啤酒灌装生产线所需设备的快速选型,且使设备的利用率最大化。方法建立啤酒灌装生产线所需设备的数据库,利用Matlab软件编写粒子群智能算法,对啤酒灌装生产线的相关设备进行多目标优化,实现快速智能寻优。结果在客户要求的约束条件下,当初始化100个种群、迭代次数为8000时,生产线平衡率高达90%以上,满足了客户要求。结论文中验证了粒子群智能算法的可靠性,并得到了客户满意的求解方案。该智能算法在生产线布局初期具有很强的柔性,可以快速解决啤酒灌装生产线建厂初期设备的快速选型问题。

关 键 词:啤酒灌装生产线  设备选型  粒子群算法  多目标优化
收稿时间:2019/1/19 0:00:00
修稿时间:2019/5/10 0:00:00

Equipment Selection of Beer Filling Production Line Based on Intelligent Algorithms
JIA Rui and LI Guang.Equipment Selection of Beer Filling Production Line Based on Intelligent Algorithms[J].Packaging Engineering,2019,40(9):135-141.
Authors:JIA Rui and LI Guang
Affiliation:Tianjin University of Science and Technology, Tianjin 300222, China and Tianjin University of Science and Technology, Tianjin 300222, China
Abstract:The work aims to achieve rapid selection of equipment for beer filling production line and maximum utilization ratio of equipment with the beer filling production line as the research object based on the Particle Swarm Intelligence (PSO) algorithm. The equipment database for beer filling production line was established. With the help of MATLAB software, multi-objective optimization of equipment in beer filling production line was carried out by particle swarm optimization algorithm. Under the constraints of customer requirements, when 100 populations were initialized and the number of iterations was 8000, the balance rate of the production line reached more than 90%, which met customer requirements. The work validated the reliability of the PSO algorithm and obtains a satisfactory solution. PSO algorithm has strong flexibility in the early stage of production line layout. It can quickly solve the problem of rapid equipment selection in the early stage of beer filling production line construction.
Keywords:beer filling production line  equipment selection  particle swarm optimization  multi-objective optimization
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