Configuration of manufacturing networks by a multi-objective perspective enabled by simulation and machine learning

Elias Franziskus Detlef Auberger; Hugo Daniel Karre; Matthias Wolf; Heimo Preising; Christian Ramsauer

doi:10.1016/j.procir.2021.11.167

Configuration of manufacturing networks by a multi-objective perspective enabled by simulation and machine learning

Elias Franziskus Detlef Auberger^*, Hugo Daniel Karre, Matthias Wolf, Heimo Preising, Christian Ramsauer

^*Corresponding author for this work

Institute of Innovation and Industrial Management (3710)

Research output: Contribution to journal › Conference article › peer-review

Abstract

Manufacturing companies are facing an increasingly volatile environment today. Internal manufacturing networks are becoming ever more important for ensuring competitive survival. The objective of this research is to introduce an iterative approach for the configuration of manufacturing networks by increasing the transparency for decision making including effects on tactical and operational level. In this context a combined method with discrete event simulation and machine learning is considered. The results of the new approach were validated within a use case at an Austrian company in the railway sector by reconfiguration of the manufacturing network consisting of 4 sites.

Original language	English
Pages (from-to)	993-998
Number of pages	6
Journal	Procedia CIRP
Volume	104
DOIs	https://doi.org/10.1016/j.procir.2021.11.167
Publication status	Published - 26 Nov 2021
Event	54th CIRP Conference on Manufacturing Systems: Towards Digitalized Manufacturing 4.0 - Virtuell Duration: 22 Sept 2021 → 24 Sept 2021

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10.1016/j.procir.2021.11.167Licence: CC BY-NC-ND 4.0

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@article{7445945b9f114408ad72ac3715a641ca,

title = "Configuration of manufacturing networks by a multi-objective perspective enabled by simulation and machine learning",

abstract = "Manufacturing companies are facing an increasingly volatile environment today. Internal manufacturing networks are becoming ever more important for ensuring competitive survival. The objective of this research is to introduce an iterative approach for the configuration of manufacturing networks by increasing the transparency for decision making including effects on tactical and operational level. In this context a combined method with discrete event simulation and machine learning is considered. The results of the new approach were validated within a use case at an Austrian company in the railway sector by reconfiguration of the manufacturing network consisting of 4 sites.",

keywords = "Operations management, Manufacturing network configuration, Machine learningData Analytics, Discrete event simulation, Maintenance industry",

author = "Auberger, {Elias Franziskus Detlef} and Karre, {Hugo Daniel} and Matthias Wolf and Heimo Preising and Christian Ramsauer",

year = "2021",

month = nov,

day = "26",

doi = "10.1016/j.procir.2021.11.167",

language = "English",

volume = "104",

pages = "993--998",

journal = "Procedia CIRP",

issn = "2212-8271",

publisher = "Elsevier B.V.",

note = "54th CIRP Conference on Manufacturing Systems : Towards Digitalized Manufacturing 4.0, CIRP CMS 2021 ; Conference date: 22-09-2021 Through 24-09-2021",

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TY - JOUR

T1 - Configuration of manufacturing networks by a multi-objective perspective enabled by simulation and machine learning

AU - Auberger, Elias Franziskus Detlef

AU - Karre, Hugo Daniel

AU - Wolf, Matthias

AU - Preising, Heimo

AU - Ramsauer, Christian

PY - 2021/11/26

Y1 - 2021/11/26

N2 - Manufacturing companies are facing an increasingly volatile environment today. Internal manufacturing networks are becoming ever more important for ensuring competitive survival. The objective of this research is to introduce an iterative approach for the configuration of manufacturing networks by increasing the transparency for decision making including effects on tactical and operational level. In this context a combined method with discrete event simulation and machine learning is considered. The results of the new approach were validated within a use case at an Austrian company in the railway sector by reconfiguration of the manufacturing network consisting of 4 sites.

AB - Manufacturing companies are facing an increasingly volatile environment today. Internal manufacturing networks are becoming ever more important for ensuring competitive survival. The objective of this research is to introduce an iterative approach for the configuration of manufacturing networks by increasing the transparency for decision making including effects on tactical and operational level. In this context a combined method with discrete event simulation and machine learning is considered. The results of the new approach were validated within a use case at an Austrian company in the railway sector by reconfiguration of the manufacturing network consisting of 4 sites.

KW - Operations management

KW - Manufacturing network configuration

KW - Machine learningData Analytics

KW - Discrete event simulation

KW - Maintenance industry

U2 - 10.1016/j.procir.2021.11.167

DO - 10.1016/j.procir.2021.11.167

M3 - Conference article

SN - 2212-8271

VL - 104

SP - 993

EP - 998

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 54th CIRP Conference on Manufacturing Systems

Y2 - 22 September 2021 through 24 September 2021

ER -

Configuration of manufacturing networks by a multi-objective perspective enabled by simulation and machine learning

Abstract

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