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Pagani P, Colling D, Furmans K (2017). Neural Network-Based Genetic Job Assignment for Automated Guided Vehicles. Logistics Journal : Proceedings, Vol. 2017. (urn:nbn:de:0009-14-45917)

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%0 Journal Article
%T Neural Network-Based Genetic Job Assignment for Automated Guided Vehicles
%A Pagani, Paolo
%A Colling, Dominik
%A Furmans, Kai
%J Logistics Journal : Proceedings
%D 2017
%V 2017
%N 10
%@ 2192-9084
%F pagani2017
%X Automated guided vehicles are designed to autonomously transport material in production and warehouse environments. The loading/unloading process of the material on the vehicles occurs at dedicated stations, called material sources and destinations. Every time a vehicle is idle, a new transportation job, i.e. the transportation of some goods from a material source to a material destination, can be assigned to one of the vehicles, which represents the limiting resource. The policies, which are used for the job assignment, are several. In this paper, a new policy based on neural networks which were trained by genetic algorithms is proposed and evaluated. The results show that this new policy outperforms a policy which is a combination of the so called “First Come First Served” and the “Nearest Vehicle First” policy.
%L 620
%K automated guided vehicles AGV
%K job assignment
%K neural networks
%K genetic algorithms
%R 10.2195/lj_Proc_pagani_en_201710_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-45917
%U http://dx.doi.org/10.2195/lj_Proc_pagani_en_201710_01
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Bibtex

@Article{pagani2017,
  author = 	"Pagani, Paolo
		and Colling, Dominik
		and Furmans, Kai",
  title = 	"Neural Network-Based Genetic Job Assignment for Automated Guided Vehicles",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2017",
  volume = 	"2017",
  number = 	"10",
  keywords = 	"automated guided vehicles AGV; job assignment; neural networks; genetic algorithms",
  abstract = 	"Automated guided vehicles are designed to autonomously transport material in production and warehouse environments. The loading/unloading process of the material on the vehicles occurs at dedicated stations, called material sources and destinations. Every time a vehicle is idle, a new transportation job, i.e. the transportation of some goods from a material source to a material destination, can be assigned to one of the vehicles, which represents the limiting resource. The policies, which are used for the job assignment, are several. In this paper, a new policy based on neural networks which were trained by genetic algorithms is proposed and evaluated. The results show that this new policy outperforms a policy which is a combination of the so called ``First Come First Served'' and the ``Nearest Vehicle First'' policy.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_pagani_en_201710_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-45917"
}
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RIS

TY  - JOUR
AU  - Pagani, Paolo
AU  - Colling, Dominik
AU  - Furmans, Kai
PY  - 2017
DA  - 2017//
TI  - Neural Network-Based Genetic Job Assignment for Automated Guided Vehicles
JO  - Logistics Journal : Proceedings
VL  - 2017
IS  - 10
KW  - automated guided vehicles AGV
KW  - job assignment
KW  - neural networks
KW  - genetic algorithms
AB  - Automated guided vehicles are designed to autonomously transport material in production and warehouse environments. The loading/unloading process of the material on the vehicles occurs at dedicated stations, called material sources and destinations. Every time a vehicle is idle, a new transportation job, i.e. the transportation of some goods from a material source to a material destination, can be assigned to one of the vehicles, which represents the limiting resource. The policies, which are used for the job assignment, are several. In this paper, a new policy based on neural networks which were trained by genetic algorithms is proposed and evaluated. The results show that this new policy outperforms a policy which is a combination of the so called “First Come First Served” and the “Nearest Vehicle First” policy.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-45917
DO  - 10.2195/lj_Proc_pagani_en_201710_01
ID  - pagani2017
ER  -
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Wordbib

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ISI

PT Journal
AU Pagani, P
   Colling, D
   Furmans, K
TI Neural Network-Based Genetic Job Assignment for Automated Guided Vehicles
SO Logistics Journal : Proceedings
PY 2017
VL 2017
IS 10
DI 10.2195/lj_Proc_pagani_en_201710_01
DE automated guided vehicles AGV; job assignment; neural networks; genetic algorithms
AB Automated guided vehicles are designed to autonomously transport material in production and warehouse environments. The loading/unloading process of the material on the vehicles occurs at dedicated stations, called material sources and destinations. Every time a vehicle is idle, a new transportation job, i.e. the transportation of some goods from a material source to a material destination, can be assigned to one of the vehicles, which represents the limiting resource. The policies, which are used for the job assignment, are several. In this paper, a new policy based on neural networks which were trained by genetic algorithms is proposed and evaluated. The results show that this new policy outperforms a policy which is a combination of the so called “First Come First Served” and the “Nearest Vehicle First” policy.
ER
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  <abstract>Automated guided vehicles are designed to autonomously transport material in production and warehouse environments. The loading/unloading process of the material on the vehicles occurs at dedicated stations, called material sources and destinations. Every time a vehicle is idle, a new transportation job, i.e. the transportation of some goods from a material source to a material destination, can be assigned to one of the vehicles, which represents the limiting resource. The policies, which are used for the job assignment, are several. In this paper, a new policy based on neural networks which were trained by genetic algorithms is proposed and evaluated. The results show that this new policy outperforms a policy which is a combination of the so called “First Come First Served” and the “Nearest Vehicle First” policy.</abstract>
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Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2017, Iss. 10
Title

Neural Network-Based Genetic Job Assignment for Automated Guided Vehicles (eng)
Author Paolo Pagani, Dominik Colling, Kai Furmans
Language eng
Abstract Automated guided vehicles are designed to autonomously transport material in production and warehouse environments. The loading/unloading process of the material on the vehicles occurs at dedicated stations, called material sources and destinations. Every time a vehicle is idle, a new transportation job, i.e. the transportation of some goods from a material source to a material destination, can be assigned to one of the vehicles, which represents the limiting resource. The policies, which are used for the job assignment, are several. In this paper, a new policy based on neural networks which were trained by genetic algorithms is proposed and evaluated. The results show that this new policy outperforms a policy which is a combination of the so called “First Come First Served” and the “Nearest Vehicle First” policy. Fahrerlose Transportsysteme werden häufig für den innerbetrieblichen Materialtransport im Produktions- und Lagerumfeld genutzt. Die Be- und Entladung mit Material findet an bestimmten Stationen, den Quellen und Senken, statt. Transportaufträge führen immer von einer Quelle zu einer Senke. Diese werden den Fahrzeugen, die die begrenzte Ressource im System darstellen, zugeordnet. Dafür gibt es unterschiedliche Verfahren. In dieser Veröffentlichung wird ein neues Verfahren vorgestellt und evaluiert, das auf von genetischen Algorithmen trainierten neuronalen Netzen basiert. Die Versuche zeigen, dass das vorgestellte Verfahren bessere Ergebnisse liefert als ein Verfahren, das eine Kombination aus „First Come First Served“- und dem „Nearest Vehicle First“-Verfahren darstellt.
Subject automated guided vehicles AGV, job assignment, neural networks, genetic algorithms
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-45917
DOI https://doi.org/10.2195/lj_Proc_pagani_en_201710_01