A Grouping Genetic Algorithm for the Intercell Scheduling Problem

Shuai Wang; Shaofeng Du; Tao Ma; Dongni Li

doi:10.1109/COASE.2018.8560518

A Grouping Genetic Algorithm for the Intercell Scheduling Problem

Shuai Wang, Shaofeng Du, Tao Ma, Dongni Li

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

To solve intercell scheduling problem in industrial environments, heuristic rules are becoming popular due to the simplicity and efficiency. Grouping decisions in decision blocks is an efficient way to make the use of heuristic rules. A decision block generation algorithm based on grouping genetic algorithm (DBGA) is proposed in this paper. In DBGA, both the size and the constitution of each decision block are evolved together. Non-sequential entities are grouped into decision blocks and rules are assigned to decision blocks simultaneously. Comparative experiments are conducted with different structures of decision blocks. The results verify the effectiveness of DBGA.

Original language	English
Title of host publication	2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
Publisher	IEEE Computer Society
Pages	956-961
Number of pages	6
ISBN (Electronic)	9781538635933
DOIs	https://doi.org/10.1109/COASE.2018.8560518
Publication status	Published - 4 Dec 2018
Event	14th IEEE International Conference on Automation Science and Engineering, CASE 2018 - Munich, Germany Duration: 20 Aug 2018 → 24 Aug 2018

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2018-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	14th IEEE International Conference on Automation Science and Engineering, CASE 2018
Country/Territory	Germany
City	Munich
Period	20/08/18 → 24/08/18

Access to Document

10.1109/COASE.2018.8560518

Cite this

Wang, S., Du, S., Ma, T., & Li, D. (2018). A Grouping Genetic Algorithm for the Intercell Scheduling Problem. In 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018 (pp. 956-961). Article 8560518 (IEEE International Conference on Automation Science and Engineering; Vol. 2018-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2018.8560518

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title = "A Grouping Genetic Algorithm for the Intercell Scheduling Problem",

abstract = "To solve intercell scheduling problem in industrial environments, heuristic rules are becoming popular due to the simplicity and efficiency. Grouping decisions in decision blocks is an efficient way to make the use of heuristic rules. A decision block generation algorithm based on grouping genetic algorithm (DBGA) is proposed in this paper. In DBGA, both the size and the constitution of each decision block are evolved together. Non-sequential entities are grouped into decision blocks and rules are assigned to decision blocks simultaneously. Comparative experiments are conducted with different structures of decision blocks. The results verify the effectiveness of DBGA.",

author = "Shuai Wang and Shaofeng Du and Tao Ma and Dongni Li",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 14th IEEE International Conference on Automation Science and Engineering, CASE 2018 ; Conference date: 20-08-2018 Through 24-08-2018",

year = "2018",

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doi = "10.1109/COASE.2018.8560518",

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Wang, S, Du, S, Ma, T & Li, D 2018, A Grouping Genetic Algorithm for the Intercell Scheduling Problem. in 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018., 8560518, IEEE International Conference on Automation Science and Engineering, vol. 2018-August, IEEE Computer Society, pp. 956-961, 14th IEEE International Conference on Automation Science and Engineering, CASE 2018, Munich, Germany, 20/08/18. https://doi.org/10.1109/COASE.2018.8560518

A Grouping Genetic Algorithm for the Intercell Scheduling Problem. / Wang, Shuai; Du, Shaofeng; Ma, Tao et al.
2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. IEEE Computer Society, 2018. p. 956-961 8560518 (IEEE International Conference on Automation Science and Engineering; Vol. 2018-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - To solve intercell scheduling problem in industrial environments, heuristic rules are becoming popular due to the simplicity and efficiency. Grouping decisions in decision blocks is an efficient way to make the use of heuristic rules. A decision block generation algorithm based on grouping genetic algorithm (DBGA) is proposed in this paper. In DBGA, both the size and the constitution of each decision block are evolved together. Non-sequential entities are grouped into decision blocks and rules are assigned to decision blocks simultaneously. Comparative experiments are conducted with different structures of decision blocks. The results verify the effectiveness of DBGA.

AB - To solve intercell scheduling problem in industrial environments, heuristic rules are becoming popular due to the simplicity and efficiency. Grouping decisions in decision blocks is an efficient way to make the use of heuristic rules. A decision block generation algorithm based on grouping genetic algorithm (DBGA) is proposed in this paper. In DBGA, both the size and the constitution of each decision block are evolved together. Non-sequential entities are grouped into decision blocks and rules are assigned to decision blocks simultaneously. Comparative experiments are conducted with different structures of decision blocks. The results verify the effectiveness of DBGA.

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A Grouping Genetic Algorithm for the Intercell Scheduling Problem

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