Regression methods for surrogate modeling of a real production system approximating the influence on inventory and tardiness

Johannes Karder, Klaus Altendorfer, Andreas Beham, Andreas Josef Peirleitner

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)

Abstract

Simulation optimization is often conducted by applying optimization heuristics (e.g., genetic algorithms) whereby the simulation model delivers the objective function value for the respective parameter set. For real world simulation models, their evaluation time is a crucial constraint. This holds especially for material requirements planning (MRP) parameter optimization of real production systems with many products, because of an extensive search space. Approximating the objective function values by surrogate models can be applied to reduce the search space. Based on a real world production system simulation model, the performance of different regression models to identify simple surrogate models for fast objective function approximation is evaluated in this paper. Specifically, a focus is put on the relationship between the MRP parameters: lot-size and planned lead time, and the performance indicators: inventory and tardiness costs. The paper evaluates a set of simple regression models and compares their objective function fit.

Original languageEnglish
Title of host publicationWSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publicationSimulation for a Noble Cause
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2037-2048
Number of pages12
ISBN (Electronic)9781538665725
DOIs
Publication statusPublished - 31 Jan 2019
Event2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden
Duration: 9 Dec 201812 Dec 2018

Publication series

NameProceedings - Winter Simulation Conference
Volume2018-December
ISSN (Print)0891-7736

Conference

Conference2018 Winter Simulation Conference, WSC 2018
Country/TerritorySweden
CityGothenburg
Period09.12.201812.12.2018

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