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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)
Department数字工厂研究室
Conference Name7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Conference DateJuly 31 - August 4, 2017
Conference PlaceHawaii, USA
Author of SourceIEEE Robotics and Automation Society
Source Publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages701-706
Indexed ByEI ; CPCI(ISTP)
EI Accession number20183905873569
WOS IDWOS:000447628700128
Contribution Rank1
ISBN978-1-5386-0489-2
Abstract

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.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22833
Collection数字工厂研究室
Corresponding AuthorZhang H(张浩)
AffiliationDept. of Information Service and Intelligent Control, Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang 110016, China
Recommended Citation
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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