LAPSE:2019.0875
Published Article
LAPSE:2019.0875
An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer
Zhonghua Han, Quan Zhang, Haibo Shi, Jingyuan Zhang
July 31, 2019
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.
Keywords
flexible flow shop scheduling, improved compact genetic algorithm, multi-queue limited buffers, probability density function of the Gaussian distribution
Suggested Citation
Han Z, Zhang Q, Shi H, Zhang J. An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer. (2019). LAPSE:2019.0875
Author Affiliations
Han Z: Department of Digital Factory, Shenyang Institute of Automation, the Chinese Academy of Sciences (CAS), Shenyang 110016, China; Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China; Key Laboratory of Network
Zhang Q: Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Shi H: Department of Digital Factory, Shenyang Institute of Automation, the Chinese Academy of Sciences (CAS), Shenyang 110016, China; Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Inte
Zhang J: Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China
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Journal Name
Processes
Volume
7
Issue
5
Article Number
E302
Year
2019
Publication Date
2019-05-21
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr7050302, Publication Type: Journal Article
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LAPSE:2019.0875
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doi:10.3390/pr7050302
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Jul 31, 2019
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Jul 31, 2019
 
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Calvin Tsay
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