Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments

Alfredo Alan Flores Saldivar, Cindy Goh, Yun Li, Hongnian Yu, Yi Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)

Abstract

Today s factory involves more services and customisation. A paradigm shift is towards 'Industry 4.0' (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment.

LanguageEnglish
Title of host publicationSKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications
Place of PublicationPiscataway, NJ.
PublisherIEEE
Pages79-86
Number of pages8
ISBN (Electronic)9781509032976
DOIs
Publication statusPublished - 1 May 2017
Event10th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2016 - Chengdu, Chengdu, China
Duration: 15 Dec 201617 Dec 2016
http://fusion-edu.eu/SKIMA2016/

Conference

Conference10th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2016
Abbreviated title(SKIMA 2016)
CountryChina
CityChengdu
Period15/12/1617/12/16
Internet address

Fingerprint

Fuzzy clustering
Industry
Insurance
Pattern recognition
Artificial intelligence
Industrial plants
Railroad cars
Genetic algorithms
Productivity
Specifications
Big data
Customization
Costs
Mass production
Informatics
Customer needs

Keywords

  • big data analytics
  • fuzzy clustering
  • genetic search
  • Industry 4.0
  • smart design
  • Smart manufacturing

Cite this

Saldivar, A. A. F., Goh, C., Li, Y., Yu, H., & Chen, Y. (2017). Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments. In SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications (pp. 79-86). [7916201] Piscataway, NJ.: IEEE. https://doi.org/10.1109/SKIMA.2016.7916201
Saldivar, Alfredo Alan Flores ; Goh, Cindy ; Li, Yun ; Yu, Hongnian ; Chen, Yi. / Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments. SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications. Piscataway, NJ. : IEEE, 2017. pp. 79-86
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abstract = "Today s factory involves more services and customisation. A paradigm shift is towards 'Industry 4.0' (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment.",
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Saldivar, AAF, Goh, C, Li, Y, Yu, H & Chen, Y 2017, Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments. in SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications., 7916201, IEEE, Piscataway, NJ., pp. 79-86, 10th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2016, Chengdu, China, 15/12/16. https://doi.org/10.1109/SKIMA.2016.7916201

Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments. / Saldivar, Alfredo Alan Flores; Goh, Cindy; Li, Yun; Yu, Hongnian; Chen, Yi.

SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications. Piscataway, NJ. : IEEE, 2017. p. 79-86 7916201.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Saldivar AAF, Goh C, Li Y, Yu H, Chen Y. Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments. In SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications. Piscataway, NJ.: IEEE. 2017. p. 79-86. 7916201 https://doi.org/10.1109/SKIMA.2016.7916201