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Soltani A, Stonis M, Overmeyer L (2019). Development of a Case-Based Reasoning expert system for the disturbance management in automated guided vehicle systems. Logistics Journal : Proceedings, Vol. 2019. (urn:nbn:de:0009-14-49931)

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%0 Journal Article
%T Development of a Case-Based Reasoning expert system for the disturbance management in automated guided vehicle systems
%A Soltani, Ali
%A Stonis, Malte
%A Overmeyer, Ludger
%J Logistics Journal : Proceedings
%D 2019
%V 2019
%N 12
%@ 2192-9084
%F soltani2019
%X Automated guided vehicle systems (AGVS) are an essential part of modern intralogistics. So far, the major part of the design cycle (implementation and operation) of an AGVS demands human expertise. Especially, the manually executed management of occurring disturbances leads to high maintenance costs since it often requires the consultation of experts. Therefore, the following paper discusses the development of a Case-Based Reasoning (CBR) expert system for assisting the disturbance management in AGVS. The development is sectioned into three major parts: (1) generation of the case-base, (2) development of the algorithms for case retrieval, case adaptation and retaining new cases and (3) the validation of the expert system. The generation of the case-base and the training data for the expert system is done by simulating the real production layout of a German white good manufacturer using the simulation environment Visual Components. The solutions for the simulated disturbances as well as the adaptation algorithms are based on knowledge extracted from system experts.
%L 620
%K System
%K automated guided vehicles AGV
%K case-based reasoning
%K disturbance management
%K expert systems
%R 10.2195/lj_Proc_soltani_en_201912_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-49931
%U http://dx.doi.org/10.2195/lj_Proc_soltani_en_201912_01
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Bibtex

@Article{soltani2019,
  author = 	"Soltani, Ali
		and Stonis, Malte
		and Overmeyer, Ludger",
  title = 	"Development of a Case-Based Reasoning expert system for the disturbance management in automated guided vehicle systems",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2019",
  volume = 	"2019",
  number = 	"12",
  keywords = 	"System; automated guided vehicles AGV; case-based reasoning; disturbance management; expert systems",
  abstract = 	"Automated guided vehicle systems (AGVS) are an essential part of modern intralogistics. So far, the major part of the design cycle (implementation and operation) of an AGVS demands human expertise. Especially, the manually executed management of occurring disturbances leads to high maintenance costs since it often requires the consultation of experts. Therefore, the following paper discusses the development of a Case-Based Reasoning (CBR) expert system for assisting the disturbance management in AGVS. The development is sectioned into three major parts: (1) generation of the case-base, (2) development of the algorithms for case retrieval, case adaptation and retaining new cases and (3) the validation of the expert system. The generation of the case-base and the training data for the expert system is done by simulating the real production layout of a German white good manufacturer using the simulation environment Visual Components. The solutions for the simulated disturbances as well as the adaptation algorithms are based on knowledge extracted from system experts.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_soltani_en_201912_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-49931"
}
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TY  - JOUR
AU  - Soltani, Ali
AU  - Stonis, Malte
AU  - Overmeyer, Ludger
PY  - 2019
DA  - 2019//
TI  - Development of a Case-Based Reasoning expert system for the disturbance management in automated guided vehicle systems
JO  - Logistics Journal : Proceedings
VL  - 2019
IS  - 12
KW  - System
KW  - automated guided vehicles AGV
KW  - case-based reasoning
KW  - disturbance management
KW  - expert systems
AB  - Automated guided vehicle systems (AGVS) are an essential part of modern intralogistics. So far, the major part of the design cycle (implementation and operation) of an AGVS demands human expertise. Especially, the manually executed management of occurring disturbances leads to high maintenance costs since it often requires the consultation of experts. Therefore, the following paper discusses the development of a Case-Based Reasoning (CBR) expert system for assisting the disturbance management in AGVS. The development is sectioned into three major parts: (1) generation of the case-base, (2) development of the algorithms for case retrieval, case adaptation and retaining new cases and (3) the validation of the expert system. The generation of the case-base and the training data for the expert system is done by simulating the real production layout of a German white good manufacturer using the simulation environment Visual Components. The solutions for the simulated disturbances as well as the adaptation algorithms are based on knowledge extracted from system experts.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-49931
DO  - 10.2195/lj_Proc_soltani_en_201912_01
ID  - soltani2019
ER  -
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Wordbib

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ISI

PT Journal
AU Soltani, A
   Stonis, M
   Overmeyer, L
TI Development of a Case-Based Reasoning expert system for the disturbance management in automated guided vehicle systems
SO Logistics Journal : Proceedings
PY 2019
VL 2019
IS 12
DI 10.2195/lj_Proc_soltani_en_201912_01
DE System; automated guided vehicles AGV; case-based reasoning; disturbance management; expert systems
AB Automated guided vehicle systems (AGVS) are an essential part of modern intralogistics. So far, the major part of the design cycle (implementation and operation) of an AGVS demands human expertise. Especially, the manually executed management of occurring disturbances leads to high maintenance costs since it often requires the consultation of experts. Therefore, the following paper discusses the development of a Case-Based Reasoning (CBR) expert system for assisting the disturbance management in AGVS. The development is sectioned into three major parts: (1) generation of the case-base, (2) development of the algorithms for case retrieval, case adaptation and retaining new cases and (3) the validation of the expert system. The generation of the case-base and the training data for the expert system is done by simulating the real production layout of a German white good manufacturer using the simulation environment Visual Components. The solutions for the simulated disturbances as well as the adaptation algorithms are based on knowledge extracted from system experts.
ER
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Mods

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  <abstract>Automated guided vehicle systems (AGVS) are an essential part of modern intralogistics. So far, the major part of the design cycle (implementation and operation) of an AGVS demands human expertise. Especially, the manually executed management of occurring disturbances leads to high maintenance costs since it often requires the consultation of experts. Therefore, the following paper discusses the development of a Case-Based Reasoning (CBR) expert system for assisting the disturbance management in AGVS. The development is sectioned into three major parts: (1) generation of the case-base, (2) development of the algorithms for case retrieval, case adaptation and retaining new cases and (3) the validation of the expert system. The generation of the case-base and the training data for the expert system is done by simulating the real production layout of a German white good manufacturer using the simulation environment Visual Components. The solutions for the simulated disturbances as well as the adaptation algorithms are based on knowledge extracted from system experts.</abstract>
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    <topic>System</topic>
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Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2019, Iss. 12
Title

Development of a Case-Based Reasoning expert system for the disturbance management in automated guided vehicle systems (eng)
Author Ali Soltani, Malte Stonis, Ludger Overmeyer
Language eng
Abstract Automated guided vehicle systems (AGVS) are an essential part of modern intralogistics. So far, the major part of the design cycle (implementation and operation) of an AGVS demands human expertise. Especially, the manually executed management of occurring disturbances leads to high maintenance costs since it often requires the consultation of experts. Therefore, the following paper discusses the development of a Case-Based Reasoning (CBR) expert system for assisting the disturbance management in AGVS. The development is sectioned into three major parts: (1) generation of the case-base, (2) development of the algorithms for case retrieval, case adaptation and retaining new cases and (3) the validation of the expert system. The generation of the case-base and the training data for the expert system is done by simulating the real production layout of a German white good manufacturer using the simulation environment Visual Components. The solutions for the simulated disturbances as well as the adaptation algorithms are based on knowledge extracted from system experts. Fahrerlose Transportsysteme (FTS) sind ein wesentlicher Bestandteil der modernen Intralogistik. Der Designzyklus für die Einführung von FTS sowie das Störungsmanagement sind überwiegend manuelle – von Experten durchgeführte – Prozesse. Diese manuellen Prozesse sind durch die Notwendigkeit von Expertenkonsultationen kostenaufwendig. Speziell im Bereich des Störungsmanagements können lange Ausfallzeiten hohe Kosten verursachen. In diesem Beitrag wird daher die Entwicklung eines Case-Based Reasoning (CBR) Expertensystems zur Unterstützung der Lösungsfindung für auftretende Störungen in FTS diskutiert. Die Entwicklung ist in drei wesentliche Bereiche gegliedert: (1) Generierung der Fallbasis, (2) Entwicklung der Algorithmen zur Suche ähnlicher Fälle, Adaption und Speicherung neuer Fälle und (3) Validierung des Expertensystems. Die Generierung der Fallbasis des Expertensystems, erfolgt durch die Simulation des realen Produktionslayouts eines deutschen Weißwarenherstellers, mit Hilfe der Simulationsumgebung Visual Components. Die Lösungen der simulierten Störungen sowie die Adaptionsalgorithmen basieren auf Erkenntnissen aus Experteninterviews.
Subject System, automated guided vehicles AGV, case-based reasoning, disturbance management, expert systems
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-49931
DOI https://doi.org/10.2195/lj_Proc_soltani_en_201912_01