An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 数字工厂研究室

	An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer
	Zhang Q(张权)3; Han ZH(韩忠华)1,2,3,4; Zhang, Jingyuan 3; Shi HB(史海波)1,2,4
刊名	PROCESSES
	2019
卷号	7 期号:5 页码:1-24
关键词	flexible flow shop scheduling multi-queue limited buffers improved compact genetic algorithm probability density function of the Gaussian distribution
ISSN号	2227-9717
产权排序	1
英文摘要	Flow 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.
资助项目	Liaoning 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]
WOS关键词	OPTIMIZATION
WOS研究方向	Engineering
语种	英语
WOS记录号	WOS:000470965600061
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/24948]
专题	沈阳自动化研究所_数字工厂研究室
通讯作者	Zhang Q(张权)
作者单位	1.Institutes for Robotics and IntelligentManufacturing, Chinese Academy of Sciences, Shenyang 110016, China 2.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, China 3.Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China 4.Department of Digital Factory, Shenyang Institute of Automation, the Chinese Academy of Sciences (CAS), Shenyang 110016, China
推荐引用方式 GB/T 7714	Zhang Q,Han ZH,Zhang, Jingyuan,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	Zhang Q,Han ZH,Zhang, Jingyuan,&Shi HB.(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	Zhang Q,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.