Abstract

Demand prediction plays a key role in supply chain management of fresh agricultural products enterprises and its algorithm research is a hotspot for the researchers related. A new algorithm for demand prediction of supply chain management of fresh agricultural products is advanced based on BP neural network and immune genetic particle swarm optimization algorithm. First, the deficiencies of traditional BP demand prediction models are analyzed. Second, the BP neural network and immune genetic particle swarm optimization algorithm are integrated and some measures are taken to overcome the deficiencies of traditional BP demand prediction models and calculation flows of the presented algorithm are redesigned. Finally, the presented algorithm is realized with the data from certain fresh agricultural products supply chain and the experimental results verify that the new algorithm can improve effectiveness and validity of demand prediction for fresh agricultural products supply chain.

Keywords:

BP neural network, demand prediction, fresh agricultural products, immune genetic particle swarm optimization algorithm, supply chain management,

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.