Machine-Learning-Based Approach for Parameterizing Material Flow Simulation Models

Vernickel, Kilian; Brunner, Laura; Hoellthaler, Georg; Sansivieri, Giuseppe; Härdtlein, Christian; Trauner, Ludwig; Bank, Lukas; Fischer, Jan; Berg, Julia

doi:10.1016/j.procir.2020.04.018

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…However, the underlying data structure is limited to one certain type of subprocess [12]. Also, there is one approach in which data sources from more different layers is considered: in addition to the fourth and fifth, also the second layer (PLC) is taken into account for the prediction of KPI of the ILS [10]. However, this approach only addresses the particularities of process simulation models.…”

Section: Discussionmentioning

confidence: 99%

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A Classification of Data Structures for Process Analysis in Internal Logistics

Wuennenberg,

Haid,

Fottner

2023

Communications in Computer and Information Science

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Data Science plays a crucial role in driving new approaches to process optimization. With the increasing complexity of internal logistics systems, dataoriented methods have become essential in addressing the challenges that arise. However, standardized process analytics frameworks are lacking due to the heterogeneity of the underlying processes and the resulting data. This article aims to address this complexity by presenting a categorization of internal logistics data, consolidating the current state of the art. The categorization takes into account both real-world and scientifically proposed data architectures, providing a comprehensive overview. It includes a classification of comparative data fields based on their importance, the associated internal logistics processes, and potential usage scenarios. This classification is designed to cater to different use cases, such as diagnostics or prescriptive analytics. By presenting this categorization, the article enables practitioners to effectively leverage generated process data in a more goal-oriented manner. It empowers them to conduct suitable analyses tailored to their specific needs and objectives, based on the provided data architectures. In summary, this article offers valuable insights into internal logistics data categorization, providing a framework for practitioners to make informed decisions and optimize processes using data-driven approaches.

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Section: Discussionmentioning

confidence: 99%

“…Several recent research contributions have addressed the issue of data science in the field of ILS [2,[10][11][12][13]. However, given their focus on certain individual aspects of this ample domain, the applied data structures are highly problem-specific and thus unable to consider all facets of this field.…”

Section: Objectivementioning

confidence: 99%

A Classification of Data Structures for Process Analysis in Internal Logistics

Wuennenberg,

Haid,

Fottner

2023

Communications in Computer and Information Science

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show abstract

“…Available data fields were considered in this model, but it is still possible that other (causal) influence factors can just not be tracked with the available data resources. Compared to existing approaches with a stronger focus on a more limited application scenario such as [9], there is still potential in terms of accuracy. Hence, further progress in process digitization yields the potential to increase the accuracy of the prediction.…”

Section: Discussionmentioning

confidence: 99%

“…One example for this is the parametrization of a simulation model with ML. Based on production data, important parameters are deduced automatically which helps simulation engineers to save time [9]. Furthermore, this simulation model can be used for process optimization in combination with business process modeling [10].…”

Section: State Of the Artmentioning

confidence: 99%