Details
| Original language | English |
|---|---|
| Pages (from-to) | 267-291 |
| Number of pages | 25 |
| Journal | Schmalenbach Journal of Business Research |
| Volume | 76 |
| Issue number | 2 |
| Early online date | 30 Apr 2024 |
| Publication status | Published - Jun 2024 |
Abstract
Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.
Keywords
- Buffer allocation, C45, C61, Flow line, JEL classification, M11, Milk run, Neural network, Performance Evaluation
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Economics, Econometrics and Finance(all)
- Business, Management and Accounting(all)
- Management of Technology and Innovation
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In: Schmalenbach Journal of Business Research, Vol. 76, No. 2, 06.2024, p. 267-291.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Using Recurrent Neural Networks for the Performance Analysis and Optimization of Stochastic Milkrun-Supplied Flow Lines
AU - Südbeck, Insa
AU - Mindlina, Julia
AU - Schnabel, André
AU - Helber, Stefan
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/6
Y1 - 2024/6
N2 - Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.
AB - Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.
KW - Buffer allocation
KW - C45
KW - C61
KW - Flow line
KW - JEL classification
KW - M11
KW - Milk run
KW - Neural network
KW - Performance Evaluation
UR - http://www.scopus.com/inward/record.url?scp=85191755399&partnerID=8YFLogxK
U2 - 10.1007/s41471-024-00183-5
DO - 10.1007/s41471-024-00183-5
M3 - Article
AN - SCOPUS:85191755399
VL - 76
SP - 267
EP - 291
JO - Schmalenbach Journal of Business Research
JF - Schmalenbach Journal of Business Research
SN - 0341-2687
IS - 2
ER -