Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules

Jens Heger; Thomas Voß

doi:10.1016/j.procir.2019.03.281

Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules

Jens Heger
, Thomas Voß

Forschungszentrum Digitale Transformation

Publikation: Beiträge in Zeitschriften › Konferenzaufsätze in Fachzeitschriften › Forschung › Begutachtung

16 Zitate (Scopus)

Abstract

The complexity of flexible job shop problems increases significantly when using autonomous guided vehicles (AGVs) for material handling. In this study, priority rules - commonly known for their simplicity and small computation time - for sequencing operations, routing jobs and dispatching vehicles are applied. Based on a discrete event simulation study with stochastic inter-arrival times, an artificial neural network is trained to learn interaction effects between the combination of different rules for sequencing, dispatching, routing, and the resulting system performance. Based on the trained network the combination of rules is optimized, reducing the mean tardiness of the jobs under varying system performance.

Originalsprache	Englisch
Zeitschrift	Procedia CIRP
Jahrgang	81
Seiten (von - bis)	1136-1141
Seitenumfang	6
ISSN	2212-8271
DOIs	https://doi.org/10.1016/j.procir.2019.03.281
Publikationsstatus	Erschienen - 01.01.2019
Veranstaltung	Conference on Manufacturing Systems - CIRP 2019: Manufacturing Systems for the Future Societies - Grand Hotel Union, Ljubljana, Slowenien Dauer: 12.06.2019 → 14.06.2019 Konferenznummer: 52 https://www.cirp-cms2019.org/

Bibliographische Notiz

Publisher Copyright:
© 2019 The Authors. Published by Elsevier Ltd.

Fachgebiete und Schlagwörter

Ingenieurwissenschaften

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik
Wirtschaftsingenieurwesen und Fertigungstechnik

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10.1016/j.procir.2019.03.281Lizenz: CC BY-NC-ND

DownloadEndgültige, publizierte Fassung, 1,22 MBLizenz: CC BY-NC-ND

Optimierung von dezentralen Ansätzen zur Belegung fahrerloser Transportsysteme
Voß, T. (Wissenschaftliche Projektleiter*in)
01.04.17 → …
Projekt: Dissertationsprojekt

Konferenzvortrag in der Session "Automated Guided Vehicles"
Voß, T. (Präsentator*in) & Heger, J. (Ko-Autor*in)
13.06.2019
Aktivität: Vorträge und Gastvorlesungen › Konferenzvorträge › Forschung

Dieses zitieren

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abstract = "The complexity of flexible job shop problems increases significantly when using autonomous guided vehicles (AGVs) for material handling. In this study, priority rules - commonly known for their simplicity and small computation time - for sequencing operations, routing jobs and dispatching vehicles are applied. Based on a discrete event simulation study with stochastic inter-arrival times, an artificial neural network is trained to learn interaction effects between the combination of different rules for sequencing, dispatching, routing, and the resulting system performance. Based on the trained network the combination of rules is optimized, reducing the mean tardiness of the jobs under varying system performance.",

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KW - Engineering

KW - AGVs

KW - Job shop

KW - Neural network

KW - Regression

UR - https://www.scopus.com/pages/publications/85068434505

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DO - 10.1016/j.procir.2019.03.281

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JF - Procedia CIRP

T2 - Conference on Manufacturing Systems - CIRP 2019

Y2 - 12 June 2019 through 14 June 2019

ER -

Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules

Abstract

Bibliographische Notiz

Fachgebiete und Schlagwörter

ASJC Scopus Sachgebiete

Zugriff auf Dokument

Fingerprint

Projekte

Optimierung von dezentralen Ansätzen zur Belegung fahrerloser Transportsysteme

Aktivitäten

Konferenzvortrag in der Session "Automated Guided Vehicles"

Dieses zitieren