Artificial neural networks in supply chain management, a review-Reference-Cited by-同舟云学术

Artificial neural networks in supply chain management, a review

Published:2023-11 Issue: Volume:1 Page:179-196
ISSN:2949-9488
Container-title:Journal of Economy and Technology
language:en
Short-container-title:Journal of Economy and Technology

Author:

Soori Mohsen^ORCID,Arezoo Behrooz,Dastres Roza

Publisher

Elsevier BV

Reference134 articles.

1. Data analytics in the supply chain management: Review of machine learning applications in demand forecasting;Aamer;Oper. Supply Chain Manag.: Int. J.,2020

2. An optimal method for supply chain logistics management based on neural network;Abdallah;CMC-Comput. Mater. Continua,2022

3. Demand forecasting in supply chain: the impact of demand volatility in the presence of promotion;Abolghasemi;Comput. Ind. Eng.,2020

4. Improved supply chain management based on hybrid demand forecasts;Aburto;Appl. Soft Comput.,2007

5. Designing an optimal inventory management model for the blood supply chain: synthesis of reusable simulation and neural network;Ahmadimanesh;Medicine,2020

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimizing solar photovoltaic farm-based cogeneration systems with artificial intelligence (AI) and Cascade compressed air energy storage for stable power generation and peak shaving: A Japan-focused case study;Applied Energy;2025-01

2. Neural network-based hybrid modeling approach incorporating Bayesian optimization with industrial soft sensor application;Knowledge-Based Systems;2024-10

3. Application of a Neural Network Model to Short-Term Water Demand Forecasting;The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024);2024-09-12

4. Application of spectroscopic technology with machine learning in Chinese herbs from seeds to medicinal materials: The case of genus Paris;Journal of Pharmaceutical Analysis;2024-09

5. A multi-energy production system utilizing an absorption refrigeration cycle, and a PEM electrolyzer powered by geothermal energy: Thermoeconomic assessment and optimization;Renewable Energy;2024-08