A hybrid LSTM method for forecasting demands of medical items in humanitarian operations-Reference-Cited by-同舟云学术

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A hybrid LSTM method for forecasting demands of medical items in humanitarian operations

Published:2024-02-13 Issue:17 Volume:62 Page:6046-6063
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Hasni Marwa¹,Babai M. Zied²,Rostami-Tabar Bahman³

Affiliation:

1. Optimisation et Analyse des Systèmes Industriels et de Service, Ecole Nationale d’Ingénieurs de Tunis, Tunis, Tunisie

2. Center of Excellence in Supply Chain – CESIT, Kedge Business School, Talence, France

3. Cardiff Business School, Cardiff University, Wales, United Kingdom

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2306904

Reference46 articles.

1. Forecasting Spare Parts Sporadic Demand Using Traditional Methods and Machine Learning – A Comparative Study;Adur Kannan B.;SMU Data Science Review,2020

2. On the empirical performance of (T,s,S) heuristics

3. On the empirical performance of some new neural network methods for forecasting intermittent demand

4. Integrated demand forecasting and planning model for repairable spare part: an empirical investigation

5. Pattern recognition with SVM and dual-tree complex wavelets

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