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Event-driven dynamic job shop scheduling execution based on improved Genetic Algorithm and Ontology
Xue LL(薛玲玲); Wang P(王鹏); Cheng HB(程海波); Zeng P(曾鹏); Yu HB(于海斌)
Department工业控制网络与系统研究室
Conference Name2017 Chinese Automation Congress (CAC2017) & China Intelligent Manufacturing International Conference (CIMIC2017)
Conference DateOctober 20-22, 2017
Conference PlaceJinan, China
Source Publication2017 Chinese Automation Congress (CAC2017) & China Intelligent Manufacturing International Conference (CIMIC2017)
PublisherIEEE
Publication PlaceNew York
2017
Pages6430-6435
Indexed ByEI ; CPCI(ISTP)
EI Accession number20183005610140
WOS IDWOS:000427816106093
Contribution Rank1
ISBN978-1-5386-3524-7
KeywordDynamic Job Shop Scheduling Problems Improved Genetic Algorithm Ontology Module
Abstract

This paper proposes an improved Genetic Algorithm (IGA}-combined with ontology module to sol\'c dynamic job shop scheduling problem (DJSSP). The objective function of this scheduling problem is to minimize a weighted sum method of maximum complete time (makespan) and mean waiting time to periodically plan production. This scheduling method is applied to a flexible manufacturing system. A reschedule strategy is utilized to solve dynamic disturbances happened during manufacturing process. Experimental results show that schedule can be repaired efficiently and correctly without obviously affecting the scheduling.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21327
Collection工业控制网络与系统研究室
Corresponding AuthorXue LL(薛玲玲)
Affiliation1.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Xue LL,Wang P,Cheng HB,et al. Event-driven dynamic job shop scheduling execution based on improved Genetic Algorithm and Ontology[C]. New York:IEEE,2017:6430-6435.
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