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Optimierung einer stochastischen MRP-Simulation unter Anwendung der Bayes’schen Optimierung

  • This paper explores whether the Bayesian optimization algorithms GPEI, TurBO and SAASBO are effective for stochastic material requirements planning simulations. It includes a comparison of other methods, with a focus on the convergence speed, a key factor in simulation-based optimization. The study uses a simple material requirement planning simulation model that is progressively expanded in complexity by adding products and levels to the bill of materials. This results in a high-dimensional optimization problem, which poses a significant challenge for simulation-based optimization. The Bayesian optimization methods are compared at each level of complexity to determine if they produce satisfactory results. Additionally, the convergence speed is analyzed in relation to method and complexity. A genetic algorithm, CMA-ES, and Sobol serve as benchmarks for the Bayesian optimization methods.

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Metadaten
Author:Philipp Zmijewski, Nicolas Meseth
Title (German):Optimierung einer stochastischen MRP-Simulation unter Anwendung der Bayes’schen Optimierung
URL:https://www.db-thueringen.de/receive/dbt_mods_00057779
DOI:https://doi.org/10.22032/dbt.57779
ISBN:978-3-86360-276-5
Parent Title (German):Simulation in Produktion und Logistik
Title Additional (English):Optimization of a Stochastic MRP Simulation Using Bayesian Optimization
Publisher:Universitätsverlag Ilmenau
Place of publication:Ilmenau
Editor:Sören Bergmann, Niclas Feldkamp, Rainer Souren, Steffen Straßburger
Document Type:Conference Proceeding
Language:German
Year of Completion:2023
Release Date:2024/05/23
Tag:Bayes'sche Optimierung; MRP; simulationsbasierte Optimierung
Volume:2023
First Page:373
Last Page:382
Note:
ASIM Fachtagung Simulation in Produktion und Logistik, 13.-15.09.2023, Technische Universität Ilmenau

Open Access
Faculties:Fakultät AuL
DDC classes:600 Technik, Medizin, angewandte Wissenschaften / 650 Management
Review Status:Veröffentlichte Fassung/Verlagsversion