Machine Learning-Supported Planning of Lead Times in Job Shop Manufacturing

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

In order to ensure adherence to schedules, knowledge of planned lead times (LT) is crucial for success. In practice, however, rigid planning methods are often used which cannot adequately reflect constantly changing environmental influences (e.g. fluctuations in the daily workload). Particularly in job shop production, precise planning of LT is difficult to implement. This paper therefore examines whether existing machine learning (ML) approaches, in particular supervised learning methods, in production planning can support LT scheduling in job shop production to generate added value. The paper enhances existing research by comparing deep artificial neural networks with ensemble methods (e.g. random forest, boosting decision trees). The applied approach bases on the Cross Industry Standard Process for Data Mining (CRISP-DM), which was created by a consortium of companies. Finally, the evaluation through an exemplary job shop production shows that the present work contributes to mastering the planned LT. In particular, the ML model, boosting decision trees and deep artificial neural networks show significant improvements in planning quality. This practical reference has not yet been addressed comprehensively in the literature.

Original languageEnglish
Title of host publicationAdvances in Production Management Systems. The Path to Digital Transformation and Innovation of Production Management Systems - IFIP WG 5.7 International Conference, APMS 2020, Proceedings : The Path to Digital Transformation and Innovation of Production Management Systems IFIP WG 5.7 International Conference, APMS 2020 Novi Sad, Serbia, August 30 – September 3, 2020 Proceedings, Part I
EditorsBojan Lalic, Ugljesa Marjanovic, Vidosav Majstorovic, Gregor von Cieminski, David Romero
Number of pages8
Volume1
Place of PublicationCham
PublisherSpringer Verlag
Publication date2020
Pages363-370
ISBN (print)978-3-030-57992-0
ISBN (electronic)978-3-030-57993-7
DOIs
Publication statusPublished - 2020
EventInternational IFIP WG 5.7 Conference on Advances in Production Management Systems: The path to digital transformation and innovation of production management systems - University of Novi Sad, Novi Sad, Serbia
Duration: 30.08.202003.09.2020

    Research areas

  • Engineering - Machine learning, Production planning & control approaches, Job shop production, Lead times

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