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Pagani P, Colling D, Furmans K (2018). A Neural Network-Based Algorithm with Genetic Training for a Combined Job and Energy Management for AGVs. Logistics Journal : Proceedings, Vol. 2018. (urn:nbn:de:0009-14-47433)
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%0 Journal Article %T A Neural Network-Based Algorithm with Genetic Training for a Combined Job and Energy Management for AGVs %A Pagani, Paolo %A Colling, Dominik %A Furmans, Kai %J Logistics Journal : Proceedings %D 2018 %V 2018 %N 01 %@ 2192-9084 %F pagani2018 %X Automated guided vehicles are designed for internal material transport in production and warehouse environments. To do this, transport orders must be assigned to the vehicles. In addition, the vehicles often have an electric drive. The batteries required for this are discharged during operation. Therefore, it must be decided when the vehicles must go to a charging station. This control option is often ignored and the vehicles are only sent for loading when the battery has (almost) completely discharged. In this work, a procedure that simultaneously solves the assignment of jobs and the decision when a vehicle should drive to a charging station is presented and evaluated. It is based on neural networks trained by genetic algorithms. The evaluation shows that the presented method delivers better results than a method that combines the "First-Come-First-Served" and the "Nearest-Vehicle-First" methods and in which the charging processes are controlled by a fixed battery threshold. %L 620 %K automated guided vehicles AGV %K genetic algorithms %K job assignment %K neural networks %K energy management %R 10.2195/lj_Proc_pagani_en_201811_01 %U http://nbn-resolving.de/urn:nbn:de:0009-14-47433 %U http://dx.doi.org/10.2195/lj_Proc_pagani_en_201811_01Download
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@Article{pagani2018,
author = "Pagani, Paolo
and Colling, Dominik
and Furmans, Kai",
title = "A Neural Network-Based Algorithm with Genetic Training for a Combined Job and Energy Management for AGVs",
journal = "Logistics Journal : Proceedings",
year = "2018",
volume = "2018",
number = "01",
keywords = "automated guided vehicles AGV; genetic algorithms; job assignment; neural networks; energy management",
abstract = "Automated guided vehicles are designed for internal material transport in production and warehouse environments. To do this, transport orders must be assigned to the vehicles. In addition, the vehicles often have an electric drive. The batteries required for this are discharged during operation. Therefore, it must be decided when the vehicles must go to a charging station. This control option is often ignored and the vehicles are only sent for loading when the battery has (almost) completely discharged. In this work, a procedure that simultaneously solves the assignment of jobs and the decision when a vehicle should drive to a charging station is presented and evaluated. It is based on neural networks trained by genetic algorithms. The evaluation shows that the presented method delivers better results than a method that combines the ``First-Come-First-Served'' and the ``Nearest-Vehicle-First'' methods and in which the charging processes are controlled by a fixed battery threshold.",
issn = "2192-9084",
doi = "10.2195/lj_Proc_pagani_en_201811_01",
url = "http://nbn-resolving.de/urn:nbn:de:0009-14-47433"
}
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TY - JOUR AU - Pagani, Paolo AU - Colling, Dominik AU - Furmans, Kai PY - 2018 DA - 2018// TI - A Neural Network-Based Algorithm with Genetic Training for a Combined Job and Energy Management for AGVs JO - Logistics Journal : Proceedings VL - 2018 IS - 01 KW - automated guided vehicles AGV KW - genetic algorithms KW - job assignment KW - neural networks KW - energy management AB - Automated guided vehicles are designed for internal material transport in production and warehouse environments. To do this, transport orders must be assigned to the vehicles. In addition, the vehicles often have an electric drive. The batteries required for this are discharged during operation. Therefore, it must be decided when the vehicles must go to a charging station. This control option is often ignored and the vehicles are only sent for loading when the battery has (almost) completely discharged. In this work, a procedure that simultaneously solves the assignment of jobs and the decision when a vehicle should drive to a charging station is presented and evaluated. It is based on neural networks trained by genetic algorithms. The evaluation shows that the presented method delivers better results than a method that combines the "First-Come-First-Served" and the "Nearest-Vehicle-First" methods and in which the charging processes are controlled by a fixed battery threshold. SN - 2192-9084 UR - http://nbn-resolving.de/urn:nbn:de:0009-14-47433 DO - 10.2195/lj_Proc_pagani_en_201811_01 ID - pagani2018 ER -Download
Wordbib
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ISI
PT Journal AU Pagani, P Colling, D Furmans, K TI A Neural Network-Based Algorithm with Genetic Training for a Combined Job and Energy Management for AGVs SO Logistics Journal : Proceedings PY 2018 VL 2018 IS 01 DI 10.2195/lj_Proc_pagani_en_201811_01 DE automated guided vehicles AGV; genetic algorithms; job assignment; neural networks; energy management AB Automated guided vehicles are designed for internal material transport in production and warehouse environments. To do this, transport orders must be assigned to the vehicles. In addition, the vehicles often have an electric drive. The batteries required for this are discharged during operation. Therefore, it must be decided when the vehicles must go to a charging station. This control option is often ignored and the vehicles are only sent for loading when the battery has (almost) completely discharged. In this work, a procedure that simultaneously solves the assignment of jobs and the decision when a vehicle should drive to a charging station is presented and evaluated. It is based on neural networks trained by genetic algorithms. The evaluation shows that the presented method delivers better results than a method that combines the "First-Come-First-Served" and the "Nearest-Vehicle-First" methods and in which the charging processes are controlled by a fixed battery threshold. ERDownload
Mods
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<abstract>Automated guided vehicles are designed for internal material transport in production and warehouse environments. To do this, transport orders must be assigned to the vehicles. In addition, the vehicles often have an electric drive. The batteries required for this are discharged during operation. Therefore, it must be decided when the vehicles must go to a charging station. This control option is often ignored and the vehicles are only sent for loading when the battery has (almost) completely discharged. In this work, a procedure that simultaneously solves the assignment of jobs and the decision when a vehicle should drive to a charging station is presented and evaluated. It is based on neural networks trained by genetic algorithms. The evaluation shows that the presented method delivers better results than a method that combines the "First-Come-First-Served" and the "Nearest-Vehicle-First" methods and in which the charging processes are controlled by a fixed battery threshold.</abstract>
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Full Metadata
| Bibliographic Citation | Logistics Journal : referierte Veröffentlichungen, Vol. 2018, Iss. 01 |
|---|---|
| Title |
A Neural Network-Based Algorithm with Genetic Training for a Combined Job and Energy Management for AGVs (eng) |
| Author | Paolo Pagani, Dominik Colling, Kai Furmans |
| Language | eng |
| Abstract | Automated guided vehicles are designed for internal material transport in production and warehouse environments. To do this, transport orders must be assigned to the vehicles. In addition, the vehicles often have an electric drive. The batteries required for this are discharged during operation. Therefore, it must be decided when the vehicles must go to a charging station. This control option is often ignored and the vehicles are only sent for loading when the battery has (almost) completely discharged. In this work, a procedure that simultaneously solves the assignment of jobs and the decision when a vehicle should drive to a charging station is presented and evaluated. It is based on neural networks trained by genetic algorithms. The evaluation shows that the presented method delivers better results than a method that combines the "First-Come-First-Served" and the "Nearest-Vehicle-First" methods and in which the charging processes are controlled by a fixed battery threshold. Fahrerlose Transportsysteme dienen dem innerbetrieblichen Materialtransport im Produktion- und Lagerumfeld. Dafür müssen den Fahrzeugen Trans-portaufträge zugeordnet werden. Außerdem haben die Fahrzeuge oft einen elektrischen Antrieb. Die dafür nötigen Akkubatterien werden im Betrieb entladen, sodass zusätzlich entschieden werden muss, wann die Fahrzeuge zu einer Ladestation fahren sollen. Diese Steuerungsmöglichkeit wird oft ignoriert, sodass die Fahrzeuge nur zum Laden geschickt werden, wenn sich die Batterie (fast) vollständig entladen hat. In dieser Veröffentlichung wird ein Verfahren vorgestellt und evaluiert, das die Auftragszuordnung sowie die Entscheidung, wann ein Fahrzeug zu einer Ladestation fahren soll, gleichzeitig löst und 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 und bei dem Aufladevorgänge nur eingeleitet werden, wenn die Fahrzeugbatterie einen Grenzwert unterschreitet. |
| Subject | automated guided vehicles AGV, genetic algorithms, job assignment, neural networks, energy management |
| DDC | 620 |
| Rights | fDPPL |
| URN: | urn:nbn:de:0009-14-47433 |
| DOI | https://doi.org/10.2195/lj_Proc_pagani_en_201811_01 |