Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/283154 
Year of Publication: 
2022
Series/Report no.: 
Hannover Economic Papers (HEP) No. 703
Publisher: 
Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, Hannover
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.
Subjects: 
Recurrent neural networks
milkrun material supply
stochastic flow lines
gradient search
simulated annealing
JEL: 
C44
C45
M11
Document Type: 
Working Paper

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