Time series imaging-based deep learning method for inventory control

Data-driven newsvendor methods offer promising solutions for estimating replenishment quantity within complex markets by leveraging demand-related features. However, most features utilized by these methods rely on manual selection, and some important unobservable features cannot be thoroughly utiliz...

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Veröffentlicht in:International journal of general systems Jg. 54; H. 3; S. 271 - 297
Hauptverfasser: Tian, Yu-Xin, Zhang, Chuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 03.04.2025
Taylor & Francis LLC
Schlagworte:
Artificial neural networks
Control data (computers)
Data-driven method
Decision making
Deep learning
Imaging
Inventory control
Multilayer perceptrons
Time series
time series imaging
ISSN:0308-1079, 1563-5104
Online-Zugang:Volltext
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Zusammenfassung:Data-driven newsvendor methods offer promising solutions for estimating replenishment quantity within complex markets by leveraging demand-related features. However, most features utilized by these methods rely on manual selection, and some important unobservable features cannot be thoroughly utilized. These issues might result in the loss of some crucial information, thus incurring considerable cost in these decision-making results, meaning that there is still much room for improvement. To remedy this, we propose a time series imaging-based deep learning method, which automatically extracts crucial features alongside those selected manually. This method maximizes the advantages of time series imaging, convolutional neural networks, bidirectional long short-term memory networks, and multilayer perceptron networks. Experiments with four diverse product time series and real-world data show that our approach reduces the average newsvendor problem cost by 16.33% to 33.43% compared to the best benchmark, highlighting its robustness. Additionally, each component within our method significantly contributes to decision-making.
Bibliographie:ObjectType-Article-1
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ISSN:0308-1079
1563-5104
DOI:10.1080/03081079.2024.2379360