2022
Oliver Antons
Distributing decision-making authority: autonomous entities in manufacturing networks PhD Thesis
Rheinisch-Westfälische Technische Hochschule Aachen, 2022, (Veröffentlicht auf dem Publikationsserver der RWTH Aachen University; Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2022).
Abstract | Links | BibTeX | Tags: Autonomy, Autonomy & Decision-making Authority, Decision-making authority, Discrete-event simulation, Multi-agent system, Production planning and control
@phdthesis{Antons:856980,
title = {Distributing decision-making authority: autonomous entities in manufacturing networks},
author = {Oliver Antons},
url = {https://oliver.antons.eu/research/decision-making-authority/ , Autonomy \& Decision-making Authority
https://publications.rwth-aachen.de/record/856980 , RWTH Publication Server},
doi = {10.18154/RWTH-2022-11291},
year = {2022},
date = {2022-12-16},
urldate = {2022-12-16},
pages = {1 Online-Ressource : Illustrationen},
publisher = {RWTH Aachen University},
address = {Aachen},
school = {Rheinisch-Westf\"{a}lische Technische Hochschule Aachen},
abstract = {Industrial production was faced with increasing challenges in the last years. Market volatility, rising energy costs and disrupted supply networks resulted in an ever-increasing information variability, which decreases productivity and complicates production planning and control (PPC). Moreover, the ever-increasing demand for, and differentiation through customization also makes planning more difficult. At the same time manufacturing networks have seen a further computerization on a machine level by the introduction of cyber-physical systems (CPS). Capable to process information, gather sensor data locally and communicate within a network, these machine provide an enormous increase in potentials for manufacturing networks. Thus, the technical requirements for distributed production control approaches are fulfilled, based on CPS acting as autonomous entities within a manufacturing network. Such distributed production control approaches feature a number of interesting characteristics, which are often quite contrary to established concepts of traditional, centralized production control. In the literature, many research streams are concerned with the advantages and disadvantages of both centralized and distributed control. While many articles provide deep insights into the workings of specialized control approaches for specific manufacturing environments, an overarching framework allowing a holistic comparison between the two fundamental production control approaches is lacking. In this thesis, the term decision-making authority is introduced to describe the level of autonomy an entity is allowed to exhibit with regard to the potential decisions it could make. Furthermore, both centralized and distributed production control approaches for manufacturing networks based on potentially autonomous entities are explored. In the former case, every entity but one central controller is not allowed to exhibit any decision-making authority, acting purely as command recipients. In the latter case, however, the aforementioned entities have a predefined degree of decision-making authority, enabling them to make certain decisions of the production scheduling on their own. Based on environment variables derived by extensive literature review, a sophisticated simulation framework is developed in form of a multi-agent based discrete-event simulation (MAS-DES). This simulation framework represents all objects of a manufacturing network, such as machines and products as agents. These agents can either follow a global plan, derived from a mixed-integer linear program modeling a centralized production control approach, or act autonomously within the scope of their respective decision-making authority in a distributed production control approach. The main part of this thesis consists of five research articles, presented in Chapters II - VI. Chapter II reviews the historic ply between centralized and decentralized control, followed by a structured literature review regarding autonomy in production planning and control, manufacturing and related research streams. Extending this, Chapter III studies the difference in information scopes of different classes of potentially autonomous entities in a manufacturing network. Chapter IV provides guidance to both researchers and practitioners alike by introducing a scheduling complexity framework, based on environment variables derived from the literature. A multi-agent based discrete-event simulation is utilized to validate the framework quantitatively. Following, Chapter V extends the simulations to study the influence of a manufacturing network’s topology on its aptitude for both centralized and distributed production control approaches. Chapter VI explores synergistic potentials between machine learning and distributed production control for manufacturing networks. Lastly, the thesis ends with a conclusion summarizing results, noting limitations and presenting avenues for future research.},
note = {Ver\"{o}ffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westf\"{a}lische Technische
Hochschule Aachen, 2022},
keywords = {Autonomy, Autonomy \& Decision-making Authority, Decision-making authority, Discrete-event simulation, Multi-agent system, Production planning and control},
pubstate = {published},
tppubtype = {phdthesis}
}
Industrial production was faced with increasing challenges in the last years. Market volatility, rising energy costs and disrupted supply networks resulted in an ever-increasing information variability, which decreases productivity and complicates production planning and control (PPC). Moreover, the ever-increasing demand for, and differentiation through customization also makes planning more difficult. At the same time manufacturing networks have seen a further computerization on a machine level by the introduction of cyber-physical systems (CPS). Capable to process information, gather sensor data locally and communicate within a network, these machine provide an enormous increase in potentials for manufacturing networks. Thus, the technical requirements for distributed production control approaches are fulfilled, based on CPS acting as autonomous entities within a manufacturing network. Such distributed production control approaches feature a number of interesting characteristics, which are often quite contrary to established concepts of traditional, centralized production control. In the literature, many research streams are concerned with the advantages and disadvantages of both centralized and distributed control. While many articles provide deep insights into the workings of specialized control approaches for specific manufacturing environments, an overarching framework allowing a holistic comparison between the two fundamental production control approaches is lacking. In this thesis, the term decision-making authority is introduced to describe the level of autonomy an entity is allowed to exhibit with regard to the potential decisions it could make. Furthermore, both centralized and distributed production control approaches for manufacturing networks based on potentially autonomous entities are explored. In the former case, every entity but one central controller is not allowed to exhibit any decision-making authority, acting purely as command recipients. In the latter case, however, the aforementioned entities have a predefined degree of decision-making authority, enabling them to make certain decisions of the production scheduling on their own. Based on environment variables derived by extensive literature review, a sophisticated simulation framework is developed in form of a multi-agent based discrete-event simulation (MAS-DES). This simulation framework represents all objects of a manufacturing network, such as machines and products as agents. These agents can either follow a global plan, derived from a mixed-integer linear program modeling a centralized production control approach, or act autonomously within the scope of their respective decision-making authority in a distributed production control approach. The main part of this thesis consists of five research articles, presented in Chapters II - VI. Chapter II reviews the historic ply between centralized and decentralized control, followed by a structured literature review regarding autonomy in production planning and control, manufacturing and related research streams. Extending this, Chapter III studies the difference in information scopes of different classes of potentially autonomous entities in a manufacturing network. Chapter IV provides guidance to both researchers and practitioners alike by introducing a scheduling complexity framework, based on environment variables derived from the literature. A multi-agent based discrete-event simulation is utilized to validate the framework quantitatively. Following, Chapter V extends the simulations to study the influence of a manufacturing network’s topology on its aptitude for both centralized and distributed production control approaches. Chapter VI explores synergistic potentials between machine learning and distributed production control for manufacturing networks. Lastly, the thesis ends with a conclusion summarizing results, noting limitations and presenting avenues for future research.
2021
Oliver Antons; Julia C. Arlinghaus
Adaptive self-learning distributed and centralized control approaches for smart factories Proceedings Article
In: pp. 1577-1582, 2021, ISSN: 2212-8271, (54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0).
Abstract | Links | BibTeX | Tags: Autonomy, Autonomy & Decision-making Authority, Cyber-physical system, Data analytics, Decision-making, Discrete-event simulation, Distributed control, Industry 4.0, Multi-agent system, Self-learning, Smart factory
@inproceedings{ANTONS20211577,
title = {Adaptive self-learning distributed and centralized control approaches for smart factories},
author = {Oliver Antons and Julia C. Arlinghaus},
url = {https://www.sciencedirect.com/science/article/pii/S2212827121011641},
doi = {https://doi.org/10.1016/j.procir.2021.11.266},
issn = {2212-8271},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Procedia CIRP},
volume = {104},
pages = {1577-1582},
abstract = {The increasing application of cyber-physical systems creates a manufacturing environment in which the technical requirements for distributed control approaches, self-learning systems and analytics of previously untapped data are given. While distributed control approaches are capable to evaluate this information locally and react immediately, centralized approaches react inertly to analyzed machine performance data. In this paper, we study the performance and ability to address the ever increasing challenges in industry of both types of control approaches within an established multi-agent based discrete event simulation.},
note = {54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0},
keywords = {Autonomy, Autonomy \& Decision-making Authority, Cyber-physical system, Data analytics, Decision-making, Discrete-event simulation, Distributed control, Industry 4.0, Multi-agent system, Self-learning, Smart factory},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing application of cyber-physical systems creates a manufacturing environment in which the technical requirements for distributed control approaches, self-learning systems and analytics of previously untapped data are given. While distributed control approaches are capable to evaluate this information locally and react immediately, centralized approaches react inertly to analyzed machine performance data. In this paper, we study the performance and ability to address the ever increasing challenges in industry of both types of control approaches within an established multi-agent based discrete event simulation.
Oliver Antons; Julia C. Arlinghaus
Distributed control for Industry 4.0 -A comparative simulation study Proceedings Article
In: pp. 516-521, 2021, ISSN: 2405-8963, (17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021).
Abstract | Links | BibTeX | Tags: Autonomy & Decision-making Authority, Control, Discrete-event simulation, Distributed control, Make-to-order, Manufacturing planning, Multi-agent system
@inproceedings{ANTONS2021516,
title = {Distributed control for Industry 4.0 -A comparative simulation study},
author = {Oliver Antons and Julia C. Arlinghaus},
url = {https://www.sciencedirect.com/science/article/pii/S2405896321007801},
doi = {https://doi.org/10.1016/j.ifacol.2021.08.059},
issn = {2405-8963},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IFAC-PapersOnLine},
volume = {54},
number = {1},
pages = {516-521},
abstract = {The recent decades have seen various market changes that industrial manufactures need to address. Increasing individualization of products and rapid demand fluctuations require new control approaches. In this article, we study the performance of centralized and distributed control approaches with regard to work in progress in a make-to-order environment. We utilize a multi-agent system in a discrete-event simulation to model several control approaches and provide various insights into potential benefits of distributed control concepts given rapidly fluctuating manufacturing demand.},
note = {17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021},
keywords = {Autonomy \& Decision-making Authority, Control, Discrete-event simulation, Distributed control, Make-to-order, Manufacturing planning, Multi-agent system},
pubstate = {published},
tppubtype = {inproceedings}
}
The recent decades have seen various market changes that industrial manufactures need to address. Increasing individualization of products and rapid demand fluctuations require new control approaches. In this article, we study the performance of centralized and distributed control approaches with regard to work in progress in a make-to-order environment. We utilize a multi-agent system in a discrete-event simulation to model several control approaches and provide various insights into potential benefits of distributed control concepts given rapidly fluctuating manufacturing demand.
Oliver Antons; Julia C. Arlinghaus
Learning Distributed Control for Job Shops-A Comparative Simulation Study Proceedings Article
In: Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA 2020, pp. 193–202, Springer International Publishing 2021.
Abstract | Links | BibTeX | Tags: Autonomy & Decision-making Authority, Discrete-event simulation, Job-shop scheduling, Multi-agent system, Production planning and control, Self-learning
@inproceedings{antons2021learning,
title = {Learning Distributed Control for Job Shops-A Comparative Simulation Study},
author = {Oliver Antons and Julia C. Arlinghaus},
url = {https://link.springer.com/book/10.1007/978-3-030-69373-2},
doi = {10.1007/978-3-030-69373-2},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA 2020},
pages = {193--202},
organization = {Springer International Publishing},
abstract = {This paper studies the potentials of learning and benefits of local data processing in a distributed control setting. We deploy a multi-agent system in the context of a discrete-event simulation to model distributed control for a job shop manufacturing system with variable processing times and multi-stage production processes. Within this simulation, we compare queue length estimation as dispatching rule against a variation with learning capability, which processes additional historic data on a machine agent level, showing the potentials of learning and coordination for distributed control in PPC.},
keywords = {Autonomy \& Decision-making Authority, Discrete-event simulation, Job-shop scheduling, Multi-agent system, Production planning and control, Self-learning},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper studies the potentials of learning and benefits of local data processing in a distributed control setting. We deploy a multi-agent system in the context of a discrete-event simulation to model distributed control for a job shop manufacturing system with variable processing times and multi-stage production processes. Within this simulation, we compare queue length estimation as dispatching rule against a variation with learning capability, which processes additional historic data on a machine agent level, showing the potentials of learning and coordination for distributed control in PPC.
2020
Oliver Antons; Julia C. Arlinghaus
Designing decision-making authorities for smart factories Proceedings Article
In: pp. 316–322, Elsevier, 2020.
Abstract | Links | BibTeX | Tags: Autonomy, Autonomy & Decision-making Authority, Cyber-physical system, Decision-making, Discrete-event simulation, Distributed control, Industry 4.0, Intelligent Product, Multi-agent system, Smart factory
@inproceedings{antons2020designing,
title = {Designing decision-making authorities for smart factories},
author = {Oliver Antons and Julia C. Arlinghaus},
url = {https://www.sciencedirect.com/science/article/pii/S2212827120306120},
doi = {10.1016/j.procir.2020.04.047},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Procedia CIRP},
volume = {93},
pages = {316--322},
publisher = {Elsevier},
abstract = {Smart factories are an appealing vision, addressing ever increasing challenges in industry. Driven by advances in microcontroller, sensor and networking technologies, all entities, such as machines, products, load carriers, within a smart factory could become intelligent and able to assess their own situation and to attain their own goals successfully. However, local decision may exacerbate achieving global logistics performance due to the entities intrinsic selfishness. In this paper, we explore the trade-off between local decision-making and global performance management and derive first guidelines for the situation-specific design of a distributed control authority for smart factories.},
keywords = {Autonomy, Autonomy \& Decision-making Authority, Cyber-physical system, Decision-making, Discrete-event simulation, Distributed control, Industry 4.0, Intelligent Product, Multi-agent system, Smart factory},
pubstate = {published},
tppubtype = {inproceedings}
}
Smart factories are an appealing vision, addressing ever increasing challenges in industry. Driven by advances in microcontroller, sensor and networking technologies, all entities, such as machines, products, load carriers, within a smart factory could become intelligent and able to assess their own situation and to attain their own goals successfully. However, local decision may exacerbate achieving global logistics performance due to the entities intrinsic selfishness. In this paper, we explore the trade-off between local decision-making and global performance management and derive first guidelines for the situation-specific design of a distributed control authority for smart factories.
2019
Oliver Antons; Julia C. Bendul
Decision making in industry 4.0--a comparison of distributed control approaches Proceedings Article
In: International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, pp. 329–339, Springer 2019.
Abstract | Links | BibTeX | Tags: Autonomy & Decision-making Authority, Distributed control, Graph colouring dynamics, Industry 4.0, Multi-agent system
@inproceedings{antons2019decision,
title = {Decision making in industry 4.0--a comparison of distributed control approaches},
author = {Oliver Antons and Julia C. Bendul},
url = {https://link.springer.com/chapter/10.1007/978-3-030-27477-1_25},
doi = {10.1007/978-3-030-27477-1_25},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing},
pages = {329--339},
organization = {Springer},
abstract = {This paper studies the influence of available information and coordination in distributed control systems for Industry 4.0. A purely decentralized and a hybrid heuristic are deployed in a Multi-Agent System in order to solve graph coloring dynamics. By means of simulation a multitude of instances is evaluated, showing a superior performance of the hybrid heuristic in most cases, underlining the importance of information availability and coordination in distributed control.},
keywords = {Autonomy \& Decision-making Authority, Distributed control, Graph colouring dynamics, Industry 4.0, Multi-agent system},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper studies the influence of available information and coordination in distributed control systems for Industry 4.0. A purely decentralized and a hybrid heuristic are deployed in a Multi-Agent System in order to solve graph coloring dynamics. By means of simulation a multitude of instances is evaluated, showing a superior performance of the hybrid heuristic in most cases, underlining the importance of information availability and coordination in distributed control.