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Estimating cycle time in the food process industry: Different machine learning techniques and their accuracy
University of Skövde, School of Informatics.
2019 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Ability to estimate cycle time is becoming more important for organisations in order to better utilize resources and potentially save money. The aim is to research whether three different machine learning techniques can be used to do this estimation of cycle time in a food processing environment and which one gets the highest accuracy.

Accuracy is tested with the help of three error measurements, called Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error, for each technique. The implementation of Artificial Neural Network, Decision Tree and k-Nearest Neighbor were usedwith a Python library called scikit-learn. Data from a real slaughterhouse was provided and used to train and test the techniques.

Results shows that all techniques managed to produce relatively equal estimations for this problem. The better technique ended up being decision tree reached a Mean Absolute Percentage Error of 34.77%, closely followed by KNN with 35.06%.

Place, publisher, year, edition, pages
2019. , p. 29
Keywords [en]
Machine Learning, Cycle Time, Time Estimation, Supervised Learning, Experiment
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:his:diva-17334OAI: oai:DiVA.org:his-17334DiVA, id: diva2:1332873
Subject / course
Informationsteknologi
Educational program
Information Systems - Enterpise Information Management
Supervisors
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Available from: 2019-07-02 Created: 2019-06-28 Last updated: 2019-07-02Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
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  • en-US
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  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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