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@PHDTHESIS{Antons:856980,
author = {Antons, Oliver},
othercontributors = {Peis, Britta and Hütt, Marc-Thorsten},
title = {{D}istributing decision-making authority: autonomous
entities in manufacturing networks},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2022-11291},
pages = {1 Online-Ressource : Illustrationen},
year = {2022},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2022},
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.},
cin = {815110},
ddc = {330},
cid = {$I:(DE-82)815110_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2022-11291},
url = {https://publications.rwth-aachen.de/record/856980},
}
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