Open AccessArtificial neural network based meta-heuristic for performance improvement in physical internet supply chain network
Author(s) -
Chouar Abdelsamad,
Tetouani Samir,
Aziz Soulhi,
Jamila Elalami
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v24.i2.pp1161-1172
Subject(s) - artificial neural network , computer science , heuristics , the internet , metaheuristic , artificial intelligence , supply chain , reduction (mathematics) , heuristic , machine learning , maxima and minima , task (project management) , engineering , systems engineering , mathematics , world wide web , mathematical analysis , geometry , political science , law , operating system
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.