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Optimized material flow using unsupervised time series clustering: An experimental study on the just in time supermarket for Volvo powertrain production Skövde.
University of Skövde, School of Informatics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Machine learning has achieved remarkable performance in many domains, now it promising to solve manufacturing problems — a new ongoing trend of using machine learning in industrial applications. Dealing with the material order demand in manufacturing as time-series sequences, making unsupervised time-series clustering possible to apply. This study aims to evaluate different time-series clustering approaches, algorithms, and distance measures in material flow data. Three different approaches are evaluated; statistical clustering approaches; raw based and shape-based approaches and at last feature-based approach. The objectives are to categorize the materials in the supermarket (intermediate storage area to store materials before assembling the products) into three different flows according to their time-series properties. The experimental shows that feature-based approach is performed best for the data. A features filter is applied to keep the relevant features, that catch the unique characteristics from the data the predicted output. As a conclusion data type, structure, the goal of the clustering task and the application domains are reasons that have to consider when choosing the suitable clustering approach.

Place, publisher, year, edition, pages
2019. , p. 61
Keywords [en]
Time-series clustering, unsupervised learning, optimization material flow, supermarket replenishment, extract relevant features
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:his:diva-17530OAI: oai:DiVA.org:his-17530DiVA, id: diva2:1342779
External cooperation
Volvo powertrain group
Subject / course
Informationsteknologi
Educational program
Data Science - Master’s Programme
Supervisors
Examiners
Available from: 2019-08-15 Created: 2019-08-14 Last updated: 2019-08-15Bibliographically approved

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fulltext(2425 kB)24 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf