Abstract
AbstractThe use of Third-Party Logistics (TPL) is a common practice among manufacturing companies seeking to increase profitability. However, the tender process in selecting a TPL service provider can be challenging, requiring significant effort from both the tendering company and the service provider. The latter must meticulously plan processes and calculate pricing positions while running the risk of losing the bid. This risk impedes verifying logistical feasibility and comparing different logistic concepts extensively, such as layouts, which are often work-intensive. With the ongoing progress of research toward automatic simulation model generation for material flow, it is left to answer whether such approaches can improve the planning processes of TPL service providers by using planning data to generate simulation models. Therefore, this work presents a system with an underlying ontology to generate material flow simulations by developing a model transformation methodology. The system’s functions are tested to determine whether they can support the planning process using defined case studies that cover everyday planning decisions. As a result, the system is capable of verifying the performance of planned logistic systems with minimal manual modelling efforts. This encompasses the evaluation of alternative logistical concepts for configuring the planned systems.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Reference33 articles.
1. Ait Alla, A., Kreutz, M., Rippel, D., Lütjen, M., & Freitag, M. (2020). Simulatedbased methodology for the interface configuration of cyber-physical production systems. International Journal of Production Research, 58(17), 5388–5403. https://doi.org/10.1080/00207543.2020.1778209
2. Barlas, P., & Heavey, C. (2016). Automation of input data to discrete event simulation for manufacturing: A review. International Journal of Modeling, Simulation, and Scientific Computing, 7(01), 1630001. https://doi.org/10.1142/S1793962316300016
3. Bergmann, S. (2014). Automatische generierung adaptiver modelle zur simulation von produktionssystemen (Unpublished doctoral dissertation) (p. 2013). Ilmenau: Technische Universität Ilmenau, Diss.
4. Bergmann, S., & Straßburger, S. (2015). On the use of the core manufacturing simulation data (cmsd) standard: experiences and recommendations. Universitätsbibliothek Ilmenau.
5. Centobelli, P., Cerchione, R., Murino, T., & Gallo, M. (2016). Layout and material flow optimization in digital factory. International journal of simulation modelling, 15(2), 223–235.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献