Dr. rer. pol. Oliver Antons
Dr. rer. pol. Oliver Antons
Production Systems and Automation (IEPS)
Oliver Antons studied mathematics at RWTH Aachen University in the Bachelor's and Master's programmes. He graduated with a Master's degree in Economics from RWTH Aachen University in 2022. After working as a research assistant at RWTH Aachen University, he joined the IAF in 2022 as a research associate. In 2022 he completed his PhD at the RWTH Aachen University on "Steuerungskonzepte für Produktionsanlagen" - "Control concepts for production plants'.
2025
Peer-reviewed journal article
Dynamic multi-period recycling collection routing with uncertain material quality
Cuellar-Usaquén, Daniel; Ulmer, Marlin W.; Antons, Oliver; Arlinghaus, Julia C.
In: OR spectrum - Berlin : Springer . - 2025, insges. 44 S. [Online first]
2024
Book chapter
Comparing digital twins and virtual engineering in buyer supplier relationships for complex production facilities
Janecki, Luca; Antons, Oliver; Reh, Daniel; Arlinghaus, Julia C.
In: Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments , 1st ed. 2024. - Cham : Springer Nature Switzerland ; Thürer, Matthias, S. 180-193 - (IFIP Advances in Information and Communication Technology; 733) [Konferenz: 43rd IFIP WG 5.7 International Conference, APMS 2024, Chemnitz, Germany, September 8-12, 2024]
A literature review on the cross-domain usage of digital factory twins within design time
Schröder, Adrian; Antons, Oliver; Arlinghaus, Julia C.
In: Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments , 1st ed. 2024. - Cham : Springer Nature Switzerland ; Thürer, Matthias, S. 315-329 - (IFIP Advances in Information and Communication Technology; 730) [Konferenz: 43rd IFIP WG 5.7 International Conference, APMS 2024, Chemnitz, Germany, September 8-12, 2024]
Planung und Steuerung für die digitale Produktion
Arlinghaus, Julia C.; Antons, Oliver
In: Handbuch Unternehmensorganisation , Living reference work, continuously updated edition - Wiesbaden : Springer Fachmedien ; Spath, Dieter *1952-* . - 2016, insges. 12 S.
Designing hybrid intelligence - understanding the impact of human decision-making on AI
Kessler, Melanie; Antons, Oliver; Arlinghaus, Julia C.
In: Advances in Manufacturing, Production Management and Process Control - New York : AHFE Open Access ; Mrugalska, Beata . - 2024, S. 31-39 [Konferenz: 15th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, Nice, France, 24-27 July 2024]
Perception of biases in machine learning in production research
Götte, Gesa; Antons, Oliver; Herzog, Andreas; Arlinghaus, Julia C.
In: Workshop Proceedings "AI in Production" - Leipzig : Hochschule für Technik, Wirtschaft und Kultur Leipzig ; Krockert, Martin . - 2024, insges. 11 S.
Peer-reviewed journal article
Digital twins and their implications for business models - overview and potentials
Adelsberger, Rodrigo Torres; Antons, Oliver; Arlinghaus, Julia C.
In: IFAC-PapersOnLine / Internationale Förderung für Automatische Lenkung - Frankfurt : Elsevier, Bd. 58 (2024), Heft 19, S. 409-414
Exploring the challenges of circular economy adoption - a supply chain perspective
Behnert, Anna-Kristin; Antons, Oliver; Arlinghaus, Julia C.
In: IFAC-PapersOnLine / Internationale Förderung für Automatische Lenkung - Frankfurt : Elsevier, Bd. 58 (2024), Heft 19, S. 211-216
On the verification of distributed control for multi job shop assignment problem in smart manufacturing system
Somma, Andrea; Antons, Oliver; Petrillo, Alberto; Santini, Stefania; Murino, Teresa
In: IFAC-PapersOnLine / Internationale Förderung für Automatische Lenkung - Frankfurt : Elsevier, Bd. 58 (2024), Heft 19, S. 217-222
Characterizing circular and open business models in a profit-driven environment through business model patterns
Behnert, Anna-Kristin; Antons, Oliver; Arlinghaus, Julia C.
In: Procedia computer science - Amsterdam [u.a.] : Elsevier, Bd. 232 (2024), S. 436-445
Interactions between planners’ and PPC systems - derivation of simulation scenarios with consideration of cognitive bias and disruptions
Rannertshauser, Patrick; Antons, Oliver; Arlinghaus, Julia
In: Procedia computer science - Amsterdam [u.a.] : Elsevier, Bd. 232 (2024), S. 1367-1376
Non-peer-reviewed journal article
Dynamic multi-period recycling collection routing with uncertain meterial quality
Cuellar-Usaquén, Daniel; Ulmer, Marlin Wolf; Antons, Oliver; Arlinghaus, Julia C.
In: Magdeburg: Otto-von-Guericke-Universität Magdeburg: Fakultät für Wirtschaftswissenschaft, 2024, 1 Online-Ressource (37 Seiten, 0,9 MB) - (Working paper series; Otto von Guericke Universität Magdeburg, Faculty of Economics and Management; 2024, no. 1)
2023
Book chapter
Sustainability in chemical production - multi-objective distributed control
Antons, Oliver; Benecke, Tobias; Mostaghim, Sanaz; Arlinghaus, Julia C.
In: New trends in intelligent software methodologies, tools and techniques - Amsterdam : IOS Press, Incorporated ; Fujita, Hamido . - 2023, S. 211-219 [Konferenz: 22nd International Conference on New Trends in Intelligent Software Methodology, Tools, and Techniques, SoMeT_23, Naples, Italy, 22-23 September 2023]
A generalized circular supply chain problem for multi-objective evolutionary algorithms
Benecke, Tobias; Antons, Oliver; Mostaghim, Sanaz; Arlinghaus, Julia C.
In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation - New York, NY, United States : Association for Computing Machinery . - 2023, S. 355-358 [Konferenz: Companion Conference on Genetic and Evolutionary Computation, GECCO '23 Companion, Lisbon, Portugal, July 15 - 19, 2023]
A coevolution approach for the multi-objective dircular supply chain problem
Benecke, Tobias; Antons, Oliver; Mostaghim, Sanaz; Arlinghaus, Julia C.
In: IEEE CAI 2023 / IEEE Conference on Artificial Intelligence , 2023 - Los Alamitos : IEEE, S. 222-223 [Konferenz: 2023 IEEE Conference on Artificial Intelligence, CAI 2023, Santa Clara, Califonien, USA, 05-06 June 2023]
Peer-reviewed journal article
Designing distributed decision-making authorities for smart factories - understanding the role of manufacturing network architecture
Antons, Oliver; Arlinghaus, Julia C.
In: International journal of production research - London [u.a.] : Taylor & Francis . - 2023, insges. 19 S. [Online first]
Maximum likelihood and neural network estimators for distributed production control
Antons, Oliver; Arlinghaus, Julia C.
In: IFAC-PapersOnLine / Internationale Förderung für Automatische Lenkung - Frankfurt : Elsevier, Bd. 56 (2023), Heft 2, S. 10327-10332
2022
Book chapter
Management for digitalization and Industry 4.0
Arlinghaus, Julia C.; Antons, Oliver
In: Handbook Industry 4.0 , 1st ed. 2022. - Berlin, Heidelberg : Springer Berlin Heidelberg ; Frenz, Walter, S. 927-948
Machine learning and autonomous control - a synergy for manufacturing
Antons, Oliver; Arlinghaus, Julia C.
In: Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future , 1st ed. 2022. - Cham : Springer International Publishing ; Borangiu, Theodor, S. 417-428 - (Studies in Computational Intelligence; volume 1034) [Workshop: 11th International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, SOHOMA 2021, 8-19 November 2021]
Peer-reviewed journal article
Opportunities for synchronization in manufacturing as key performance indicator
Knapp, Florian; Antons, Oliver; Arlinghaus, Julia C.
In: Procedia CIRP / CIRP - The International Academy for Production Engineering - Amsterdam [u.a.] : Elsevier, Bd. 107 (2022), S. 1467-1472 [Konferenz: 55th CIRP Conference on Manufacturing Systems 2022]
A manufacturing scheduling complexity framework and agent-based comparison of centralized and distributed control approaches
Antons, Oliver; Arlinghaus, Julia C.
In: IEEE journal of emerging and selected topics in industrial electronics - New York, NY : The Institute of Electrical and Electronics Engineers, Bd. 3 (2022), Heft 1, insges. 8 S.
Applied machine learning for production planning and control - overview and potentials
Büttner, Konstantin; Antons, Oliver; Arlinghaus, Julia C.
In: IFAC-PapersOnLine / Internationale Förderung für Automatische Lenkung - Frankfurt : Elsevier, Bd. 55 (2022), Heft 10, S. 2629-2634
Data-driven and autonomous manufacturing control in cyber-physical production systems
Antons, Oliver; Arlinghaus, Julia C.
In: Computers in industry - Amsterdam [u.a.] : Elsevier Science, Bd. 141 (2022), Artikel 103711
Distributing decision-making authority in manufacturing - review and roadmap for the factory of the future
Antons, Oliver; Arlinghaus, Julia C.
In: International journal of production research - London [u.a.] : Taylor & Francis, Bd. 60 (2022), Heft 13, S. 4342-4360, insges. 19 S. [Online first]
Scientific monograph
Distributing decision-making authority: autonomous entities in manufacturing networks
Antons, Oliver; Peis, Britta; Hütt, Marc-Thorsten
In: Aachen: Universitätsbibliothek der RWTH Aachen, Dissertation Rheinisch-Westfälische Technische Hochschule Aachen 2022, 1 Online-Ressource - (Aachen; RWTH Aachen University, 2022)
2021
Book chapter
Learning distributed control for job shops - a comparative simulation study
Antons, Oliver; Arlinghaus, Julia C.
In: Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future , 1st ed. 2021. - Cham : Springer International Publishing ; Borangiu, Theodor, S. 193-202 [Workshop: 10th International Workshop on Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, SOHOMA 2020, Paris, France, October 1-2, 2020]
Peer-reviewed journal article
Distributed control for Industry 4.0 - a comparative simulation study
Antons, Oliver; Arlinghaus, Julia C.
In: IFAC-PapersOnLine / Internationale Förderung für Automatische Lenkung - Frankfurt : Elsevier, Bd. 54 (2021), Heft 1, S. 516-521
Adaptive self-learning distributed and centralized control approaches for smart factories
Antons, Oliver; Arlinghaus, Julia C.
In: Procedia CIRP / CIRP - The International Academy for Production Engineering - Amsterdam [u.a.] : Elsevier, Bd. 104 (2021), S. 1577-1582
2020
Book chapter
Management für Digitalisierung und Industrie 4.0
Arlinghaus, Julia C.; Antons, Oliver
In: Handbuch Industrie 4.0: Recht, Technik, Gesellschaft , 1st ed. 2020. - Berlin, Heidelberg : Springer Berlin Heidelberg ; Frenz, Walter, S. 1121-1145
Autonomous production control methods - job shop simulations
Zhao, Ziqi; Antons, Oliver; Arlinghaus, Julia C.
In: Dynamics in Logistics , 1st ed. 2020. - Cham : Springer International Publishing ; Freitag, Michael, S. 227-235 - ( Advances in Intelligent Systems and Computing; volume 1026; 2nd International Conference on Human Systems Engineering and Design, IHSED2019, München, September 16-18, 2019)
Modelling autonomous production control - a guide to select the most suitable modelling approach
Antons, Oliver; Arlinghaus, Julia C.
In: Dynamics in Logistics , 1st ed. 2020. - Cham : Springer International Publishing ; Freitag, Michael, S. 245-253 - ( Advances in Intelligent Systems and Computing; volume 1026; 2nd International Conference on Human Systems Engineering and Design, IHSED2019, München, September 16-18, 2019)
Peer-reviewed journal article
Designing decision-making authorities for smart factories
Antons, Oliver; Arlinghaus, Julia C.
In: Procedia CIRP / CIRP - The International Academy for Production Engineering - Amsterdam [u.a.] : Elsevier, Bd. 93 (2020), S. 316-322 [Part of special issue: 53rd CIRP Conference on Manufacturing Systems 2020]
2019
Book chapter
Decision making in Industry 4.0 - a comparison of distributed control approaches
Antons, Oliver; Arlinghaus, Julia C.
In: Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future , 1st ed. 2020 - Cham : Springer ; Borangiu, Theodor . - 2019, S. 329-339 - (Studies in computational intelligence; 853) [Workshop: International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, SOHOMA 2019, València, 3-4 October 2019]
Peer-reviewed journal article
Supply chain risks in times of Industry 4.0 - insights from German cases
Arlinghaus, Julia; Zimmermann, Manuel; Antons, Oliver; Rosca, Eugenia
In: IFAC-PapersOnLine / Internationale Förderung für Automatische Lenkung - Frankfurt : Elsevier, Bd. 52 (2019), Heft 13, S. 1755-1760 [Konferenz: 9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019, Berlin, Germany, 28-30 August 2019]
Current projects
OpenDANS - Open Data for Sustainable and Scalable Production Research
Duration: 01.01.2024 bis 31.12.2027
Artificial intelligence (AI) methods are increasingly becoming a critical competitive factor for companies and science in Saxony-Anhalt. Universities and research institutions are providing impetus for the development of new planning, control and optimization methods and AI applications. Production and logistics companies use these methods to continuously increase efficiency, flexibility and sustainability. The OpenDANS project aims to synergistically network both players. The aim of the project is to create an open source database for real data and AI applications that companies and researchers can access free of charge. Researchers will have access to real data sets, which will significantly increase research results, particularly in terms of application proximity. Companies gain direct access to methods and analysis approaches.
Following the example of biology and medical research, such as the establishment of the Human Genome Project as an open database, OpenDANS also aims to unleash disruptive potential for production and logistics research. The resulting database will become the basis for scientists worldwide and allow them to build on each other's results. The open exchange of standardized real data sets creates synergy, improves the applicability and scalability of research results in the field of AI and digital twins. This brings considerable benefits for scientists, for Saxony-Anhalt as a science location and for the efficiency and sustainability of local companies.
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Completed projects
Productive Teaming research initiative - sub-project "Improved collaboration between man and machine in the production process"
Duration: 01.01.2023 bis 31.12.2024
In the face of rapid technological advances, the forward-looking vision of the Productive Teaming research initiative is to take human-machine collaboration to a new level. The aim is to enable a new generation of dynamic human-machine teams in production systems that can master more complex and adaptive challenges than current cyber-physical systems or human teams.
Instead of conventional production automation, which relies heavily on fixed, predefined processes, teaming production systems should focus on maintaining the flexibility and adaptability of production processes. To this end, the innate human ability to react and adapt agilely to disruptions, such as errors from previous process steps or required input quantities for semi-finished products, should also be realized at machine level. Instead of aiming for maximum automation and the associated need to standardize production processes, the "Productive Teaming" research initiative aims to enable maximum flexibility and dynamization of production, both in terms of the individual production steps and the design of the finished product (batch size 1). This paradigm shift promotes the emergence of adaptive manufacturing processes, the improvement of working conditions, greater sustainability and greater product individualization, which takes into account the challenges of Industry 5.0.
To realize these goals, intelligent systems should be improved to i) draw conclusions on a cognitive level, ii) coordinate interdependent actions for seamless, goal-oriented and coherent teamwork, iii) design joint action plans and iv) create trust and acceptance through transparency in decision-making. The resulting new level of human-machine symbiosis leads to more flexibility in the production landscape, less material and energy consumption and a general reduction in cognitive and physical stress for human employees.
b]"Productive Teaming" is a joint research initiative of Chemnitz University of Technology, Ilmenau University of Technology and OVGU Magdeburg. The three universities complement each other perfectly in the network, which is dedicated in particular to human-technology interaction and will also expand into other research areas in the future. Each of the three universities contributes its own special expertise - Chemnitz University of Technology in the field of "Human-Machine Interaction and Cognitive Systems", Ilmenau University of Technology in the field of "Intelligent Sensors and Complex Systems" and OVGU Magdeburg in the research field of "Artificial Intelligence and Digital Engineering".
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Cluster of Excellence Initiative SmartProSys: Intelligent Process Systems for Sustainable Chemical Production" Subcluster / Subproject: Dynamic Closed Loop Management for Sustainable Chemicals
Duration: 01.09.2022 bis 30.06.2024
The Magdeburg research initiative SmartProSys (Smart Process Systems Engineering) investigates methods and approaches for the sustainable transformation of chemical, mechanical and biotechnological production processes towards a sustainable circular economy for a sustainable society. Scientists from the fields of logistics, mathematics, sociology, political science and psychology are involved in SmartProSys and are looking at the possibilities for this transformation in the clusters "Systems Engineering and Computational Methods", "Supply Chain and Sustainability Management" and "Societal Support and Individual Appropriation".
Sub-project: Dynamic Closed Loop Management for Sustainable Chemicals
The Chair of Production Systems and Automation is involved in the "Supply Chain and Sustainability Management" cluster and leads the sub-project "Dynamic Closed Loop Management for Sustainable Chemicals". A sustainable circular economy in the chemical industry poses new logistical challenges: Transport emissions, procurement from sources of varying quality and sustainability in a time-critical multidimensional optimization. The sub-project aims to create suitable models and simulations to support the overarching goal of a sustainable transformation of the circular economy in chemical production.
The research network pursues the goal of excellence as defined by the Excellence Initiative of the German federal and state governments.
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