Publications

@article{doi:10.1080/00207543.2023.2217285,

title = {Designing distributed decision-making authorities for smart factories \textendash understanding the role of manufacturing network architecture},

author = {Oliver Antons and Julia C. Arlinghaus},

url = {https://doi.org/10.1080/00207543.2023.2217285},

doi = {10.1080/00207543.2023.2217285},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {International Journal of Production Research},

volume = {0},

number = {0},

pages = {1-19},

publisher = {Taylor \& Francis},

abstract = {The availability of cyber-physical systems (CPS) in modern manufacturing networks provides a multitude of interesting opportunities from a manufacturing control perspective. Providing sensors, data gathering, local computation and communication capabilities modern CPS fulfil the technical requirements to act completely autonomously in a manufacturing network. While the distribution of decision-making authority to autonomous entities is feasible given such requirements, practice often sees the monopolisation of decision-making authority for centralised control. However, distributed production control approaches might be better suited given current manufacturing challenges, ranging from unreliable supply chains over highly volatile markets, to the demand for increasingly efficient and highly customisable production. In this article, we extend an existing scheduling complexity framework which enables practitioners and researchers alike to assess the aptitude of given manufacturing networks for both centralised and distributed control. In particular, we study the influence of a manufacturing network's topology ranging from assembly line to job shops on the aforementioned aptitude, with total production costs as objective.



We utilise a multi-agent-based discrete-event simulation comparing an MILP-based centralised control approach and an autonomy based distributed control approach with weighted costs as decision function to evaluate this framework. Our results provide novel insights regarding the influence of manufacturing network topologies on the scheduling complexity of manufacturing networks.},

keywords = {Autonomous production control, Autonomy \& Decision-making Authority, Centralized control, Decision-making authority, Distributed control, Manufacturing network topology, Production planning and control},

pubstate = {published},

tppubtype = {article}

}

@article{ANTONS2022103711,

title = {Data-driven and autonomous manufacturing control in cyber-physical production systems},

author = {Oliver Antons and Julia C. Arlinghaus},

url = {https://www.sciencedirect.com/science/article/pii/S0166361522001087},

doi = {https://doi.org/10.1016/j.compind.2022.103711},

issn = {0166-3615},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Computers in Industry},

volume = {141},

pages = {103711},

abstract = {Modern manufacturing networks consist of cyber-physical systems (CPS) which offer an array of interesting capabilities, ranging from local computation over data generation to communication capabilities. As traditional control approaches fail to fully leverage these capabilities, the last decade has seen a renewed interest in distributed control approaches based on autonomous entities. In this article, we study the synergistic potentials of autonomous control and machine learning in a job-shop setting, addressing challenges of modern manufacturing such as market fluctuation and process time variance, thus leveraging the potentials of CPS in order to flexibly configure manufacturing networks and achieve cost-minimal production. We utilize a multi-agent based discrete-event simulation to compare this novel approach to a traditional heuristic, underlining the potentials and advantages of data-driven control approaches.},

keywords = {Autonomous production control, Autonomy \& Decision-making Authority, Machine learning, Neural network, Production planning and control},

pubstate = {published},

tppubtype = {article}

}

@article{Antons2021Distributing,

title = {Distributing decision-making authority in manufacturing \textendash review and roadmap for the factory of the future},

author = {Oliver Antons and Julia C. Arlinghaus},

url = {https://doi.org/10.1080/00207543.2022.2057255},

doi = {10.1080/00207543.2022.2057255},

year  = {2021},

date = {2021-07-27},

urldate = {2021-07-27},

journal = {International Journal of Production Research},

volume = {60},

number = {0},

issue = {13},

pages = {1-19},

publisher = {Taylor \& Francis},

abstract = {The question of the benefits of autonomous control is more important than ever: production managers, governments and society hope that the vision of smart and digital production systems with high flexibility and low costs may save the value adding and therefore welfare in the high wage, industrialised countries. At the same time, the discussion on the social implications of autonomous objects and decentralised control approaches is growing. Looking back on the history of production research and practice, we find that there has been a constant ply among scholars and production managers between the advantages of the two concepts of centralised and decentralised control approaches. In this article, we study the concept of autonomy in production planning and control, enabled by cyber-physical systems and the distribution of decision-making authority. Based on a profound structured literature review, we analyse the perception of autonomy, the technological requirements and the increasing complexities of modern smart manufacturing. Moreover, we find that recently several research streams suggest the advantages and benefits of autonomous control concepts compared to traditional centralised approaches based on qualitative analysis and identify a distinct lack of quantitative results.},

keywords = {Autonomous production control, Autonomy, Autonomy \& Decision-making Authority, Decentralized Control, Industry 4.0, Production planning and control},

pubstate = {published},

tppubtype = {article}

}