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Decentralized governance and artificial intelligence policy with blockchain-based voting in federated learning
C Alisdair Lee1,2, K M Chow3, H Anthony Chan1
1School of Computing and Information Sciences, Caritas Institute of Higher Education, Hong Kong SAR, China.
This study introduces a dynamic strategy simulator using AI and blockchain to reduce fruit loss in supply chains. The system significantly cuts mango losses and operational costs by optimizing delivery sequences based on forecasting.
Area of Science:
- Supply Chain Management
- Artificial Intelligence
- Blockchain Technology
Background:
- Fruit supply chains face significant losses due to inefficient handling and outdated dispatch strategies like 'first-in-first-out'.
- Lack of dynamic decision-making capabilities for frontline operators exacerbates fruit spoilage during transit.
Purpose of the Study:
- To develop a dynamic strategy simulator for optimizing fruit delivery sequences.
- To reduce fruit loss and improve cost-effectiveness in the supply chain using predictive analytics.
Main Methods:
- Implemented asynchronous federated learning (FL) on a blockchain platform with smart contracts.
- Utilized an AI and Internet of Things engine with Long Short-Term Memory (LSTM) for forecasting.
- Developed a decentralized governance AI policy for model consensus.
Main Results:
- Simulations demonstrated a significant reduction in fruit loss, with mangoes experiencing only 0.035% loss.
- Operational costs within the fruit supply chain were substantially reduced.
Conclusions:
- The proposed AI and blockchain-based dynamic strategy simulator effectively reduces fruit loss and operational costs.
- A case study of an Indonesian mango supply chain validated the approach's practical effectiveness.
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