A Farming-for-Mining-Framework to Gain Knowledge in Supply Chains

Hunker, Joachim; Wuttke, Alexander; Scheidler, Anne Antonia; Rabe, Markus

doi:10.1109/wsc52266.2021.9715372

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…102.400 simulation runs. Hunker et al [32] apply data farming in the context of supply chains. An exemplary supply chain is analyzed using an experiment plan with 257 different experiments.…”

Section: Relevant Data Farming Applicationsmentioning

confidence: 99%

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: Relevant Data Farming Applicationsmentioning

confidence: 99%

Solution Space Management to Enable Data Farming in Strategic Network Design

et al. 2023

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“…A possible application is to use the data with a subsequent knowledge discovery process, which is referred to as farming-for-mining and is detailed in [5]. Simulation is usually carried out using simulation tools.…”

Section: Introductionmentioning

confidence: 99%

LogFarm: An Open Source Graph-based Simulator for Logistics Networks

Wuttke,

Hunker,

Rabe

2024

SNE

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Logistics networks are complex systems that are often subject to simulation to gain insights. For the computer-aided simulation, simulation tools are used that are also referred to as simulators. In this article, a novel open-source simulator for logistics networks is introduced. The key feature of the tool is its graph-based mode of operation and its usage of simulation models that are stored in graph databases. The simulator is designed to be used in a data farming framework, which means it can be used to generate data along the simulation. The article discusses the general idea and concept behind the authors' graph-based simulator as well as the implementation of an open-source prototype. The prototype is evaluated through the examination of two use cases and a test on the ARGESIM C14 benchmark.

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