Publications

@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}

}