Sequence Scrambling in Aggregated Mixed-Model Production Line Modeling

Kroeger, Sebastian; Korder, Svenja; Schneider, Robin; Zaeh, Michael F.

doi:10.1109/wsc57314.2022.10015255

Cited by 1 publication

(2 citation statements)

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“…In addition to the application results, the solution space management approach also needs to be validated as a part of the data-farming-based planning approach by Kroeger et al [46], which will be the subject of future publications.…”

Section: Discussionmentioning

confidence: 99%

“…In strategic network design, many production line characteristics are uncertain. Therefore, production line failures can be estimated based on similar existing production lines using aggregated line models, as presented in [46,47]. For this industrial application, only the production line behavior of the rotor lines is varied between a worst-case and a realistic case (SC04).…”

Section: Application Of Step 2: Solution Space Planning and Controlmentioning

confidence: 99%

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Solution Space Management to Enable Data Farming in Strategic Network Design

et al. 2023

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During strategic network design, not only strategic but also operational decisions must be made long before a production network is put into operation. These include determining the location and size of inventories within the production network and setting operational parameters for production lines, such as the shift model. However, the large solution space comprising a high number of highly uncertain design parameters makes these decisions challenging without decision support. Therefore, data farming offers a potential solution, as synthetic data can be generated via the execution of multiple simulation experiments spanning the solution space and then analyzed using data mining techniques to provide data-based decision support. However, data farming has not yet been applied to strategic network design due to the lack of adequate solution space management. To address this shortcoming, this paper presents a structured solution space management approach that integrates production network-specific requirements and Design of Experiment (DoE) methods. The approach enables converting the solution space in strategic network design into individual input data sets for simulation experiments, generating a comprehensive database that can be mined for data-based decision support. The applicability and validity of the comprehensive approach are ensured via a case study in the automotive industry.

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Section: Discussionmentioning

confidence: 99%

Section: Application Of Step 2: Solution Space Planning and Controlmentioning

confidence: 99%