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 metaheuristic. 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 regarding five recent metaheuristics. The experimental results highlight the effectiveness of ISMA according to other metaheuristics for training Feed-forward Neural Networks (FNNs) to converge speed and to avoid local minima.
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