An Optimized Supply Chain Network Model Based on Modified Genetic Algorithm

ZHANG Yikun; LIU Shufen; ZHANG Xinjia

doi:10.1049/cje.2017.03.018

Volume 26 Issue 3

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ZHANG Yikun, LIU Shufen, ZHANG Xinjia, “An Optimized Supply Chain Network Model Based on Modified Genetic Algorithm,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 468-476, 2017, doi: 10.1049/cje.2017.03.018

Citation:

ZHANG Yikun, LIU Shufen, ZHANG Xinjia, “An Optimized Supply Chain Network Model Based on Modified Genetic Algorithm,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 468-476, 2017, doi: 10.1049/cje.2017.03.018

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An Optimized Supply Chain Network Model Based on Modified Genetic Algorithm

doi: 10.1049/cje.2017.03.018

College of Computer Science and Technology, Jilin University, Changchun 130012, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61472160), and Key Projects in the National Science & Technology Program during the Twelfth Five-year Plan Period (No.2014BAH29F03).

More Information

Corresponding author: ZHANG Xinjia (corresponding author) was born in Jilin Province, China, in 1977. He received his Ph.D. degree from Jilin University in 2007 in computer science and technology. Currently he is a lecturer of college of computer science and technology in Jilin University. His research interests include computer network and computer simulation technology. (Email:zhxj@jlu.edu.cn)
Received Date: 2015-02-06
Rev Recd Date: 2015-04-17
Publish Date: 2017-05-10

Abstract

Abstract

For complex multi-source, multi-product, multi-stage Supply chain network (SCN) design problem, we propose an optimization supply chain network model. We consider cash conversion cycle as an objective to this model and utilize a modified genetic algorithm to solve the problem. To describe the structure of supply chain network, we propose a new encoding method and a genetic algorithm with modified genetic operators. We use the Pareto approach to obtain the set of Pareto-optimal solutions. In order to evaluate the performance of the modified genetic algorithm and validate the model, we conduct comparisons with standard genetic algorithm and the simulated annealing genetic algorithm. Experimental results show that the modified genetic algorithm achieved better CPU time and the accuracy of the Pareto-optimal solutions than the alternative algorithms and the model was effective.
- Supply chain network (SCN),
- Genetic algorithm,
- Multi-objective optimization,
- Cash conversion cycle

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References(25)

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