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Hormes F, Siala A, Lieb C, Fottner J (2020). Fleet Sizing of Dynamically Routed In-plant Milk-run Vehicles Based on a Genetic Algorithm. Logistics Journal : Proceedings, Vol. 2020. (urn:nbn:de:0009-14-51420)

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%0 Journal Article
%T Fleet Sizing of Dynamically Routed In-plant Milk-run Vehicles Based on a Genetic Algorithm
%A Hormes, Fabian
%A Siala, Amin
%A Lieb, Christian
%A Fottner, Johannes
%J Logistics Journal : Proceedings
%D 2020
%V 2020
%N 12
%@ 2192-9084
%F hormes2020
%X In-plant milk-run (MR) systems enable efficient supply of assembly lines in small lot sizes. One major chal-lenge for MR systems are demand fluctuations and short-term changes within schedules. Dynamic control strategies aim at increasing flexibility and efficiency of MR systems in volatile environments. This paper presents an application-oriented approach for determining the fleet size of an MR system with dynamically controlled routes based on a genetic algorithm. The approach is evaluated and discussed using a case study from a commercial vehicle manufacturer. The results show that the approach ena-bles effective analytical dimensioning of MR systems with dynamic routes. In addition, the case study indicates that the implementation of dynamic routes can lead to a reduction in fleet size.
%L 620
%K Genetische Algorithmen
%K In-plant milk-run
%K Routenzugsysteme
%K Routing-Probleme
%K Steuerungsstrategien
%K control strategies
%K genetic algorithms
%K routing problems
%R 10.2195/lj_Proc_hormes_en_202012_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-51420
%U http://dx.doi.org/10.2195/lj_Proc_hormes_en_202012_01
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@Article{hormes2020,
  author = 	"Hormes, Fabian
		and Siala, Amin
		and Lieb, Christian
		and Fottner, Johannes",
  title = 	"Fleet Sizing of Dynamically Routed In-plant Milk-run Vehicles Based on a Genetic Algorithm",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2020",
  volume = 	"2020",
  number = 	"12",
  keywords = 	"Genetische Algorithmen; In-plant milk-run; Routenzugsysteme; Routing-Probleme; Steuerungsstrategien; control strategies; genetic algorithms; routing problems",
  abstract = 	"In-plant milk-run (MR) systems enable efficient supply of assembly lines in small lot sizes. One major chal-lenge for MR systems are demand fluctuations and short-term changes within schedules. Dynamic control strategies aim at increasing flexibility and efficiency of MR systems in volatile environments. This paper presents an application-oriented approach for determining the fleet size of an MR system with dynamically controlled routes based on a genetic algorithm. The approach is evaluated and discussed using a case study from a commercial vehicle manufacturer. The results show that the approach ena-bles effective analytical dimensioning of MR systems with dynamic routes. In addition, the case study indicates that the implementation of dynamic routes can lead to a reduction in fleet size.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_hormes_en_202012_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-51420"
}
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TY  - JOUR
AU  - Hormes, Fabian
AU  - Siala, Amin
AU  - Lieb, Christian
AU  - Fottner, Johannes
PY  - 2020
DA  - 2020//
TI  - Fleet Sizing of Dynamically Routed In-plant Milk-run Vehicles Based on a Genetic Algorithm
JO  - Logistics Journal : Proceedings
VL  - 2020
IS  - 12
KW  - Genetische Algorithmen
KW  - In-plant milk-run
KW  - Routenzugsysteme
KW  - Routing-Probleme
KW  - Steuerungsstrategien
KW  - control strategies
KW  - genetic algorithms
KW  - routing problems
AB  - In-plant milk-run (MR) systems enable efficient supply of assembly lines in small lot sizes. One major chal-lenge for MR systems are demand fluctuations and short-term changes within schedules. Dynamic control strategies aim at increasing flexibility and efficiency of MR systems in volatile environments. This paper presents an application-oriented approach for determining the fleet size of an MR system with dynamically controlled routes based on a genetic algorithm. The approach is evaluated and discussed using a case study from a commercial vehicle manufacturer. The results show that the approach ena-bles effective analytical dimensioning of MR systems with dynamic routes. In addition, the case study indicates that the implementation of dynamic routes can lead to a reduction in fleet size.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-51420
DO  - 10.2195/lj_Proc_hormes_en_202012_01
ID  - hormes2020
ER  -
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<b:Comments>In-plant milk-run (MR) systems enable efficient supply of assembly lines in small lot sizes. One major chal-lenge for MR systems are demand fluctuations and short-term changes within schedules. Dynamic control strategies aim at increasing flexibility and efficiency of MR systems in volatile environments. This paper presents an application-oriented approach for determining the fleet size of an MR system with dynamically controlled routes based on a genetic algorithm. The approach is evaluated and discussed using a case study from a commercial vehicle manufacturer. The results show that the approach ena-bles effective analytical dimensioning of MR systems with dynamic routes. In addition, the case study indicates that the implementation of dynamic routes can lead to a reduction in fleet size.</b:Comments>
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ISI

PT Journal
AU Hormes, F
   Siala, A
   Lieb, C
   Fottner, J
TI Fleet Sizing of Dynamically Routed In-plant Milk-run Vehicles Based on a Genetic Algorithm
SO Logistics Journal : Proceedings
PY 2020
VL 2020
IS 12
DI 10.2195/lj_Proc_hormes_en_202012_01
DE Genetische Algorithmen; In-plant milk-run; Routenzugsysteme; Routing-Probleme; Steuerungsstrategien; control strategies; genetic algorithms; routing problems
AB In-plant milk-run (MR) systems enable efficient supply of assembly lines in small lot sizes. One major chal-lenge for MR systems are demand fluctuations and short-term changes within schedules. Dynamic control strategies aim at increasing flexibility and efficiency of MR systems in volatile environments. This paper presents an application-oriented approach for determining the fleet size of an MR system with dynamically controlled routes based on a genetic algorithm. The approach is evaluated and discussed using a case study from a commercial vehicle manufacturer. The results show that the approach ena-bles effective analytical dimensioning of MR systems with dynamic routes. In addition, the case study indicates that the implementation of dynamic routes can lead to a reduction in fleet size.
ER
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  <abstract>In-plant milk-run (MR) systems enable efficient supply of assembly lines in small lot sizes. One major chal-lenge for MR systems are demand fluctuations and short-term changes within schedules. Dynamic control strategies aim at increasing flexibility and efficiency of MR systems in volatile environments. This paper presents an application-oriented approach for determining the fleet size of an MR system with dynamically controlled routes based on a genetic algorithm. The approach is evaluated and discussed using a case study from a commercial vehicle manufacturer. The results show that the approach ena-bles effective analytical dimensioning of MR systems with dynamic routes. In addition, the case study indicates that the implementation of dynamic routes can lead to a reduction in fleet size.</abstract>
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    <topic>Genetische Algorithmen</topic>
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    <topic>Routenzugsysteme</topic>
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Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2020, Iss. 12
Title

Fleet Sizing of Dynamically Routed In-plant Milk-run Vehicles Based on a Genetic Algorithm (eng)
Author Fabian Hormes, Amin Siala, Christian Lieb, Johannes Fottner
Language eng
Abstract In-plant milk-run (MR) systems enable efficient supply of assembly lines in small lot sizes. One major chal-lenge for MR systems are demand fluctuations and short-term changes within schedules. Dynamic control strategies aim at increasing flexibility and efficiency of MR systems in volatile environments. This paper presents an application-oriented approach for determining the fleet size of an MR system with dynamically controlled routes based on a genetic algorithm. The approach is evaluated and discussed using a case study from a commercial vehicle manufacturer. The results show that the approach ena-bles effective analytical dimensioning of MR systems with dynamic routes. In addition, the case study indicates that the implementation of dynamic routes can lead to a reduction in fleet size. Innerbetriebliche Routenzugsysteme (RZS) ermöglichen eine effiziente Materialversorgung von Montagelinien in kleinen Losgrößen. Eine Herausforderung für RZS sind Bedarfsschwankungen und kurzfristige Änderungen in Fahrplänen. Das Ziel von dynamischen Steuerungsstrategien ist die Erhöhung der Flexibilität und Effizienz von RZS in volatilen Umgebungen. Dieser Beitrag stellt einen anwendungsorientierten Ansatz zur Bestimmung der Flottengröße eines RZS mit dynamischen Routen basierend auf einem genetischen Algorithmus vor. Der Ansatz wird anhand einer Fallstudie eines Nutzfahrzeugherstellers evaluiert und diskutiert. Die Ergebnisse zeigen, dass der vorgestellte Ansatz eine effektive analytische Dimensionierung von RZS mit dynamischen Routen ermöglicht. Die Fallstudie legt zudem nahe, dass der Einsatz von dynamischen Routen zu einer Reduzierung der benötigten Anzahl an Fahrzeugen führen kann.
Subject Genetische Algorithmen, In-plant milk-run, Routenzugsysteme, Routing-Probleme, Steuerungsstrategien, control strategies, genetic algorithms, routing problems
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-51420
DOI https://doi.org/10.2195/lj_Proc_hormes_en_202012_01