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Multi-objective Job Shop Scheduling using a Modified Artificial Bee Colony Algorithm
Zhang H(张浩); Zhu YL(朱云龙); Ku T(库涛)
作者部门数字工厂研究室
会议名称7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
会议日期July 31 - August 4, 2017
会议地点Hawaii, USA
会议主办者IEEE Robotics and Automation Society
会议录名称2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
出版者IEEE
出版地New York
2017
页码701-706
收录类别EI ; CPCI(ISTP)
EI收录号20183905873569
WOS记录号WOS:000447628700128
产权排序1
ISBN号978-1-5386-0489-2
摘要

To optimize job shop scheduling in smelting process of high-precision copper ingot effectually, a multiobjective model with some nonlinear constraints is established according to the existing production capacity and melting technology of smelting production line for copper ingot. Two objectives of minimizing production time and penalty value for the plans not containing some orders is formulated in this model, which can meet clients' delivery date and reduce production cost. In the paper, a novel modified artificial bee colony algorithm (MABC) is proposed firstly for multi-objective job scheduling optimization according to cooperative coevolution is the concept in symbiosis theory. A new encoding rule for the algorithm is designed to solve the scheduling model of job shop effectively. The experiment results using the actual data in production show that MABC is robust and effective. MABC can obtain better solutions compared to multi-objective particle swarm optimization (MOPSO) and nondominated sorting genetic algorithm II (NSGAII) when solving this scheduling model.

语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22833
专题数字工厂研究室
通讯作者Zhang H(张浩)
作者单位Dept. of Information Service and Intelligent Control, Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang 110016, China
推荐引用方式
GB/T 7714
Zhang H,Zhu YL,Ku T. Multi-objective Job Shop Scheduling using a Modified Artificial Bee Colony Algorithm[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:701-706.
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