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

Chouar Abdelsamad (Universit ´ e´ Mohammed V-Agdal Ecole Mohammadia d’Ingenieurs)
Tetouani Samir (Universit ´ e´ Mohammed V-Agdal Ecole Mohammadia d’Ingenieurs)
Soulhi Aziz (Superior National School of Mines)
Elalami Jamila (National Center for Scientific and Technical Research)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 24, No 2: November 2021

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.