# Last-Mile Optimization Using Neural Networks
## 6 Conclusions  
Effective collaboration between the manufacturer, the supply chain partners, and the transport providers is crucial to deliver products on time as well as in appropriate condition and, therefore, to satisfy sustainability in urban areas. Different types of ML and DL approaches are introduced with their applications in the manufacturing sector. The purpose of this study is to classify product flows by category using the MLP neural network and ML approaches in order to improve supply chain operations. To do this, we first collected the dataset of the products; then, by preprocessing the raw data, we listed the comparisons among the proposed three neural networks based on an MLP neural that was trained with different parameters on the same amount of data in this experiment. Results indicate that MLP 1 obtained the best indicators of classification metrics compared with the other MLP neural networks in terms of classification accuracy, precision, recall, F1 score, error rate, and time taken to build the model. The proposed best MLP neural network is compared with ML classifiers, and a better classification effect can be obtained with the MLP neural network. The best MLP reached a classification accuracy of 97.56%. Through comparative techniques, MLP was found to be very efficient when dealing with datasets as in this case. Therefore, the results found confirm the aforementioned statement. To extend the accomplishment of this work, we plan to further enhance our methodology to more than four categories in order to reduce confusion errors between categories. Additionally, we suggest collecting more data as they help the model to understand the different classes better and minimize confusion. In such a way, we will be able to show that this work can also be applied for the classification of other products. Methodology, E.K. ; Supervision, N.N. and N.R. All authors have read and agreed to the published version of the manuscript. This research was funded by Eurometropolis of Metz. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical concerns. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline KNN & 96.45\% & 77.32\% & 59.71\% & 63.94\% \\ RF & 98\% & 90.13\% & 79.74\% & 84.09\% \\ LR & 96.4\% & 77.32\% & 57.12\% & 60.78\% \\ SVM & 96.5\% & 81.69\% & 56.53\% & 60.21\% \\ MLP & 99.76\% & 93.34\% & 79.84\% & 85.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Models’ performance across predictive maintenance dataset. * [PERSON] et al. (2018) [PERSON] [PERSON]; [PERSON]; [PERSON] Effective Product Categorization with Importance Scores and Morphological Analysis of the Titles. In Proceedings of the 30 th IEEE International Conference on Tools with Artificial Intelligence, Volos, Greece, 5-7 November 2018. * [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] [PERSON] Large-scale Multi-class and Hierarchical Product Categorization for an E-commerce Giant. In Proceedings of the 26 th International Conference on Computational Linguistics, Osaka, Japan, 11-16 December 2016. * [PERSON] et al. (2016) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Product Classification in E-Commerce using Distributional Semantics. In Proceedings of the 26 th International Conference on Computational Linguistics, Osaka, Japan, 11-16 December 2016. * [PERSON] et al. (2021) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Deep CNN-based autonomous system for safety measures in logistics transportation. _Soft Comput._**2021**, _25_, 12357-12370. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] [PERSON] _Machine Learning for Decision Making in the Improvement of Digital Press Services_; CIGI QUALITA MOSIM: Trois-Rivieres, QC, Canada, 2023. * [PERSON] (2020) [PERSON] Self-adaptive traffic and logistics flow control using learning agents and ubiquitous sensors.