Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules
Research output: Journal contributions › Conference article in journal › Research › peer-review
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In: Procedia CIRP, Vol. 81, 01.01.2019, p. 1136-1141.
Research output: Journal contributions › Conference article in journal › Research › peer-review
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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 -