<p>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.</p>
Managing the performance of port container terminals is one of the major challenges in the supply chain management. In response to this challenge, we propose a new framework to assess managers in evaluating the global performance of operations in port container terminals. The new framework integrates the Data Envelopment Analysis (DEA) and Artificial Neural Network (ANN). The DEA is used to compute the efficient score of the system. In ANN, we use the improved grey wolf optimizer based on Levy’s flights to improve learning. In order to prove the efficiency of our model, we apply the framework in 2 ports container terminal: Tangier Med Port and Casablanca Port. The result is compared to standard algorithms and outperforms the cited algorithm in order to avoid local minima. The new trainer improved grey wolf optimizer is also evaluated using four known classification datasets and on three approximation functions datasets.
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