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Wuddi P, Fottner J (2021). Self-Learning Problem Prioritization for Operating Tugger Train Systems. Logistics Journal : Proceedings, Vol. 2021. (urn:nbn:de:0009-14-54492)

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%0 Journal Article
%T Self-Learning Problem Prioritization for Operating Tugger Train Systems
%A Wuddi, Philipp
%A Fottner, Johannes
%J Logistics Journal : Proceedings
%D 2021
%V 2021
%N 17
%@ 2192-9084
%F wuddi2021
%X For the operational control of logistics systems, the application of optimization methods using self-learning algorithms is increasingly the subject of research and development. Knowledge management systems, which address the specific reaction to deviations, i. e. disturbances and fluctuations of system parameters, form a special application use case. This paper discusses in detail, how such a system can evaluate, which present deviation in the logistic system should ideally be subject to the reaction of the control system. Several ideas are part of the discussion and narrow down to four different approaches. An overall evaluation and a synthesis of the individual approaches to a universally valid and applicable approach follow. Furthermore, future possibilities for enhancement complete the paper
%L 620
%K Leitsysteme
%K Logistiksteuerung
%K control systems
%K decision-making
%K logistics control
%K operational control
%K operative Steuerung
%K selbstlernende Systeme
%K self-learning systems
%R 10.2195/lj_Proc_wuddi_en_202112_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-54492
%U http://dx.doi.org/10.2195/lj_Proc_wuddi_en_202112_01
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@Article{wuddi2021,
  author = 	"Wuddi, Philipp
		and Fottner, Johannes",
  title = 	"Self-Learning Problem Prioritization for Operating Tugger Train Systems",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2021",
  volume = 	"2021",
  number = 	"17",
  keywords = 	"Leitsysteme; Logistiksteuerung; control systems; decision-making; logistics control; operational control; operative Steuerung; selbstlernende Systeme; self-learning systems",
  abstract = 	"For the operational control of logistics systems, the application of optimization methods using self-learning algorithms is increasingly the subject of research and development. Knowledge management systems, which address the specific reaction to deviations, i. e. disturbances and fluctuations of system parameters, form a special application use case. This paper discusses in detail, how such a system can evaluate, which present deviation in the logistic system should ideally be subject to the reaction of the control system. Several ideas are part of the discussion and narrow down to four different approaches. An overall evaluation and a synthesis of the individual approaches to a universally valid and applicable approach follow. Furthermore, future possibilities for enhancement complete the paper",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_wuddi_en_202112_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-54492"
}
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RIS

TY  - JOUR
AU  - Wuddi, Philipp
AU  - Fottner, Johannes
PY  - 2021
DA  - 2021//
TI  - Self-Learning Problem Prioritization for Operating Tugger Train Systems
JO  - Logistics Journal : Proceedings
VL  - 2021
IS  - 17
KW  - Leitsysteme
KW  - Logistiksteuerung
KW  - control systems
KW  - decision-making
KW  - logistics control
KW  - operational control
KW  - operative Steuerung
KW  - selbstlernende Systeme
KW  - self-learning systems
AB  - For the operational control of logistics systems, the application of optimization methods using self-learning algorithms is increasingly the subject of research and development. Knowledge management systems, which address the specific reaction to deviations, i. e. disturbances and fluctuations of system parameters, form a special application use case. This paper discusses in detail, how such a system can evaluate, which present deviation in the logistic system should ideally be subject to the reaction of the control system. Several ideas are part of the discussion and narrow down to four different approaches. An overall evaluation and a synthesis of the individual approaches to a universally valid and applicable approach follow. Furthermore, future possibilities for enhancement complete the paper
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-54492
DO  - 10.2195/lj_Proc_wuddi_en_202112_01
ID  - wuddi2021
ER  -
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Wordbib

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ISI

PT Journal
AU Wuddi, P
   Fottner, J
TI Self-Learning Problem Prioritization for Operating Tugger Train Systems
SO Logistics Journal : Proceedings
PY 2021
VL 2021
IS 17
DI 10.2195/lj_Proc_wuddi_en_202112_01
DE Leitsysteme; Logistiksteuerung; control systems; decision-making; logistics control; operational control; operative Steuerung; selbstlernende Systeme; self-learning systems
AB For the operational control of logistics systems, the application of optimization methods using self-learning algorithms is increasingly the subject of research and development. Knowledge management systems, which address the specific reaction to deviations, i. e. disturbances and fluctuations of system parameters, form a special application use case. This paper discusses in detail, how such a system can evaluate, which present deviation in the logistic system should ideally be subject to the reaction of the control system. Several ideas are part of the discussion and narrow down to four different approaches. An overall evaluation and a synthesis of the individual approaches to a universally valid and applicable approach follow. Furthermore, future possibilities for enhancement complete the paper
ER
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Mods

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    <topic>control systems</topic>
    <topic>decision-making</topic>
    <topic>logistics control</topic>
    <topic>operational control</topic>
    <topic>operative Steuerung</topic>
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Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2021, Iss. 17
Title

Self-Learning Problem Prioritization for Operating Tugger Train Systems (eng)
Author Philipp Wuddi, Johannes Fottner
Language eng
Abstract For the operational control of logistics systems, the application of optimization methods using self-learning algorithms is increasingly the subject of research and development. Knowledge management systems, which address the specific reaction to deviations, i. e. disturbances and fluctuations of system parameters, form a special application use case. This paper discusses in detail, how such a system can evaluate, which present deviation in the logistic system should ideally be subject to the reaction of the control system. Several ideas are part of the discussion and narrow down to four different approaches. An overall evaluation and a synthesis of the individual approaches to a universally valid and applicable approach follow. Furthermore, future possibilities for enhancement complete the paper Für die operative Steuerung logistischer Systemen ist der Einsatz von Optimierungsmethoden unter der Nutzung selbstlernender Algorithmen zunehmend Gegenstand von Forschungs- und Entwicklungsaufgaben. Einen besonderen Anwendungsfall bilden an dieser Stelle selbstlernende Wissensmanagementsysteme, welche die zielgerichtete Reaktion auf Abweichungen, also Störungen und Schwankungen von Systemkennwerten, adressieren. In diesem Beitrag wird im Detail darauf eingegangen, wie ein solches System bewerten kann, auf welches in der Regelstrecke vorliegende Problem idealerweise zu reagieren ist. Hierzu werden zunächst vier verschiedene Ansätze hergeleitet und diskutiert. Anschließend erfolgt eine gesamtheitliche Bewertung und eine Synthese der einzelnen Ansätze hin zu einem allgemeingültigen bzw. allgemein anwendbaren Ansatz. Weitere Verbesserungsmöglichkeiten bilden den Abschluss des Papers.
Subject Leitsysteme, Logistiksteuerung, control systems, decision-making, logistics control, operational control, operative Steuerung, selbstlernende Systeme, self-learning systems
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-54492
DOI https://doi.org/10.2195/lj_Proc_wuddi_en_202112_01