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An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer
Han ZH(韩忠华)1,2,3,4; Zhang, Quan2; Shi HB(史海波)1,3,4; Zhang, Jingyuan2
Department数字工厂研究室
Source PublicationPROCESSES
ISSN2227-9717
2019
Volume7Issue:5Pages:1-24
Indexed BySCI
WOS IDWOS:000470965600061
Contribution Rank1
Funding OrganizationLiaoning Provincial Science Foundation, China ; Natural Science Foundation of China ; Project of Liaoning Province Education Department, China ; Shenyang Municipal Science and Technology Project, China
Keywordflexible flow shop scheduling multi-queue limited buffers improved compact genetic algorithm probability density function of the Gaussian distribution
AbstractFlow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can improve the local dispatching process for the multi-queue limited buffer. We evaluate our method through the real data from a bus manufacture production line. The results show that the proposed ICGA with local dispatching rules and is very efficient and outperforms other existing methods.
Language英语
WOS SubjectEngineering, Chemical
WOS KeywordOPTIMIZATION
WOS Research AreaEngineering
Funding ProjectLiaoning Provincial Science Foundation, China[2018106008] ; Natural Science Foundation of China[61873174] ; Project of Liaoning Province Education Department, China[LJZ2017015] ; Shenyang Municipal Science and Technology Project, China[Z18-5-102]
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24948
Collection数字工厂研究室
Corresponding AuthorZhang, Quan
Affiliation1.Department of Digital Factory, Shenyang Institute of Automation, the Chinese Academy of Sciences (CAS), Shenyang 110016, China
2.Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China
3.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, China
4.Institutes for Robotics and IntelligentManufacturing, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Han ZH,Zhang, Quan,Shi HB,et al. An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer[J]. PROCESSES,2019,7(5):1-24.
APA Han ZH,Zhang, Quan,Shi HB,&Zhang, Jingyuan.(2019).An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer.PROCESSES,7(5),1-24.
MLA Han ZH,et al."An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer".PROCESSES 7.5(2019):1-24.
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