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

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Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules. / Heger, Jens; Voß, Thomas.
in: Procedia CIRP, Jahrgang 81, 01.01.2019, S. 1136-1141.

Publikation: Beiträge in ZeitschriftenKonferenzaufsätze in FachzeitschriftenForschungbegutachtet

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@article{6ad285e2b7c54eee8604cc67d54a822a,
title = "Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules",
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.",
keywords = "Engineering, AGVs, Job shop, Neural network, Regression",
author = "Jens Heger and Thomas Vo{\ss}",
note = "Publisher Copyright: {\textcopyright} 2019 The Authors. Published by Elsevier Ltd.; Conference on Manufacturing Systems - CIRP 2019 : Manufacturing Systems for the Future Societies, CIRP CMS ; Conference date: 12-06-2019 Through 14-06-2019",
year = "2019",
month = jan,
day = "1",
doi = "10.1016/j.procir.2019.03.281",
language = "English",
volume = "81",
pages = "1136--1141",
journal = "Procedia CIRP",
issn = "2212-8271",
publisher = "Elsevier B.V.",
url = "https://www.cirp-cms2019.org/",

}

RIS

TY - JOUR

T1 - Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules

AU - Heger, Jens

AU - Voß, Thomas

N1 - Conference code: 52

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Engineering

KW - AGVs

KW - Job shop

KW - Neural network

KW - Regression

UR - http://www.scopus.com/inward/record.url?scp=85068434505&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2019.03.281

DO - 10.1016/j.procir.2019.03.281

M3 - Conference article in journal

VL - 81

SP - 1136

EP - 1141

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

T2 - Conference on Manufacturing Systems - CIRP 2019

Y2 - 12 June 2019 through 14 June 2019

ER -

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