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Estimating cycle time in the food process industry: Different machine learning techniques and their accuracy
Högskolan i Skövde, Institutionen för informationsteknologi.
2019 (engelsk)Independent thesis Basic level (degree of Bachelor), 20 poäng / 30 hpOppgave
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%.

sted, utgiver, år, opplag, sider
2019. , s. 29
Emneord [en]
Machine Learning, Cycle Time, Time Estimation, Supervised Learning, Experiment
HSV kategori
Identifikatorer
URN: urn:nbn:se:his:diva-17334OAI: oai:DiVA.org:his-17334DiVA, id: diva2:1332873
Fag / kurs
Informationsteknologi
Utdanningsprogram
Information Systems - Enterpise Information Management
Veileder
Examiner
Tilgjengelig fra: 2019-07-02 Laget: 2019-06-28 Sist oppdatert: 2019-07-02bibliografisk kontrollert

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