Enhancing Supply Chain Resilience: A Deep Learning Approach to Late Delivery Risk Prediction-Reference-Cited by-同舟云学术

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Enhancing Supply Chain Resilience: A Deep Learning Approach to Late Delivery Risk Prediction

Published:2024-05-16 Issue: Volume:8 Page:1-7
ISSN:
Container-title:2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)
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Author:

Douaioui Kaoutar¹,Oucheikh Rachid²,Mabrouki Charif³

Affiliation:

1. Faculty of Sciences and Techniques in Settat, Laboratoire Ingénierie Laboratory of Industrial Engineering, Management, and Innovation (LIMII),Settat,Morocco

2. School of Engineering, Jönköping Sweden University,Jönköping,Sweden,551 11

3. Laboratoire Ingénierie Laboratory of IndustrialEngineering, Management, and Innovation (LIMII),Faculty of Sciences and Techniques,Settat,Morocco

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10548014/10548010/10548074.pdf?arnumber=10548074

Reference23 articles.

1. A multiobjective integrated procurement, production, and distribution problem of supply chain network under fuzziness

2. « Reducing Inconsistency in Performance Analysis for Container Terminals;Fri;International Journal of Supply and Operations Management,2021

3. « Supply chain performance evaluation models, state-of-the-art and future directions »;Fri;Int. J. Eng. Adv. Technol,2019

4. Exploring Implementation of Blockchain for the Supply Chain Resilience and Sustainability of the Construction Industry in Saudi Arabia

5. A modelling framework for improving supply chain delivery performance

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