Artificial neural network based meta-heuristic for performance improvement in physical internet supply chain network

Chouar Abdelsamad, Tetouani Samir, Soulhi Aziz, Elalami Jamila


Nowadays, reducing total costs while enhancing customer satisfaction is a major task for many supply chain systems. To deal with this issue, the physical internet (PI) paradigm can be represented as a potential replacement for the current logistics system. This paper devoted the cost reduction and lead time improvement in a PI-SCN using a hybrid framework based on an artificial neural network (ANN) and an improved slime mould algorithm (ISMA). To address the performance of the proposed framework, a real-case study in Morocco is considered. The new trainer ISMA’s performance has been investigated in three approximation datasets from the University of California at Irvine (UCI) machine-learning repository regarding nine recent metaheuristics. The experimental results highlight the effectiveness of ISMA according to other meta heuristics for training feed-forward neural networks (FNNs) to converge speed and to avoid local minima.


Artificial neural networks; Slime mould algorithm; Supply chain management; Physical internet;

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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