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Open AccessArticle

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

by Zhonghua Han 1,2,3,4, Quan Zhang 2,*, Haibo Shi 1,3,4 and Jingyuan Zhang 2
1
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 Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
*
Author to whom correspondence should be addressed.
Processes 2019, 7(5), 302; https://doi.org/10.3390/pr7050302
Received: 3 April 2019 / Revised: 14 May 2019 / Accepted: 15 May 2019 / Published: 21 May 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. View Full-Text
Keywords: flexible flow shop scheduling; multi-queue limited buffers; improved compact genetic algorithm; probability density function of the Gaussian distribution flexible flow shop scheduling; multi-queue limited buffers; improved compact genetic algorithm; probability density function of the Gaussian distribution
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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. Processes 2019, 7, 302.

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