Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions

Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions

<i>Background:</i> Over the past decade, the potential advantages of employing deep learning models and leveraging auxiliary data in data-driven end-to-end (E2E) frameworks to enhance inventory decision-making have gained recognition. However, current approaches predominantly rely on fee...

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Bibliographic Details
Main Authors: Thais de Castro Moraes, Jiancheng Qin, Xue-Ming Yuan, Ek Peng Chew
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Logistics
Subjects:
inventory optimization
newsvendor problem
deep learning
CNN-LSTM
evolving algorithms
end-to-end approaches
Online Access:https://www.mdpi.com/2305-6290/7/4/79
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