摘要
为了提高第Ⅱ类机器人混流装配线系统能效,界定工业机器人5种工作状态,量化不同状态下的机器人能源消耗值,引入关停策略和考虑产品切换,以最小化最大工作时间和能源消耗为目标,构建平衡与排序联合决策双目标优化模型.设计改进的非支配排序遗传算法Ⅱ,通过同类算法对比分析,验证改进算法的有效性.结合算例,揭示关停策略和产品切换准备作业对机器人混流装配线系统的技术影响。关停策略能够减少能耗,在装配线平衡性下降时的效果愈加明显,最大能耗节约率达到16.68%;考虑产品切换准备作业的影响,有利于机器人混流装配线作业效率和能源消耗的整体优化.
In order to optimize the energy and efficiency of the type-Ⅱrobotic mixed-model assembly line system,firstly,five working states of industrial robots was defined to measure the robot energy consumption in state.A dualobjective optimization model of joint decision-making of balancing and sequencing was proposed in consideration of shutdown strategies and product switching.Its objectives were taken as minimizing the maximum working time and energy consumption.Next,the improved non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ)was designed and its validation was also verified by comparing with a similarly algorithm.Finally,the technical impact of shutdown strategy and product switching on the robotic mixed-model assembly line system was shown by example analysis.The shutdown strategy was able to bring energy savings to a certain extent,and the benefits become more obvious when the balance of the assembly line decreases,and the maximum energy saving rate reached 16.68%;the product switching in consideration was beneficial both to the operation efficiency and energy efficiency for the robotic mixed-model assembly line.
作者
孙宝凤
张新康
李根道
刘娇娇
SUN Bao-feng;ZHANG Xin-kang;LI Gen-dao;LIU Jiao-jiao(College of Transportation,Jilin University,Changchun 130022,China;School of Economics and Management,Changchun University of Science and Technology,Changchun 130012,China)
出处
《浙江大学学报(工学版)》
EI
CAS
CSCD
北大核心
2022年第6期1097-1106,共10页
Journal of Zhejiang University:Engineering Science
基金
国家自然科学基金资助项目(61873109)
吉林省自然科学基金资助项目(20210101055JC)
一汽股份技术创新资助项目(KF2020-20006)。
关键词
机器人混流装配线系统
能源消耗
关停策略
产品切换
平衡与排序优化
改进的分支配排序遗传算法Ⅱ
type-Ⅱrobotic mixed-model assembly line system
energy consumption
shutdown strategy
product switching
balancing and sequencing optimization
improved non-dominated sorting genetic algorithmⅡ