# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method  
In this study, we focus on optimizing industries' supply chain, particularly the last-mile transportation process. The classification of products is an essential element in supply chain management. It helps industries to optimize their inventory management, logistics operations, demand forecasting, and supply chain visibility, leading to improved efficiency and cost savings.  
In this paper, our work will be presented in four parts as follows:  
* Collecting and preprocessing the data.
* Splitting the dataset into training and testing data.
* Building and training models: MLP and ML techniques for products flows classification.
* Evaluating models using various metrics.  
Figure 2 illustrates the overall architecture of the proposed product flows classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Year** & **Domain** & **Approaches** \\ \hline [PERSON] and [PERSON] [(26)] & 2016 & E-Commerce & DBN and DAE \\ [PERSON] et al. [(27)] & 2016 & E-Commerce & path-wise, node-wise, and depth-wise \\ [PERSON] et al. [(12)] & 2017 & Network Traffic & MLP \\ [PERSON] et al. [(14)] & 2017 & Network Traffic & NB, Levenberg–Marquardt, and MLP \\ [PERSON] et al. [(25)] & 2018 & E-Commerce & LR and RF \\ [PERSON] et al. [(8)] & 2018 & Medical diagnosis & SVM, KNN, and DT \\ [PERSON] et al. [(11)] & 2018 & Network Traffic & MLP \\ [PERSON] et al. [(24)] & 2019 & E-Commerce & L2 AC \\ [PERSON] et al. [(23)] & 2019 & Education & MLP \\ [PERSON] and [PERSON] [(4)] & 2019 & Medical diagnosis & DT, RF, and GBT \\ [PERSON] et al. [(21)] & 2019 & NLP & LR, SVM, Ada Boost, RF, and MLP \\ [PERSON] et al. [(9)] & 2019 & Medical diagnosis & RT, NB, and K-star \\ [PERSON] et al. [(17)] & 2020 & Electric & MLP \\ [PERSON] et al. [(35)] & 2020 & Logistics & Extra Trees, AdaBoost, and SVR \\ [PERSON] et al. [(28)] & 2021 & Logistics & CNN \\ [PERSON] et al. [(13)] & 2021 & Network Traffic & NB, KNN, SVM, and DT \\ [PERSON] et al. [(3)] & 2021 & Medical diagnosis & MLP \\ [PERSON] et al. [(15)] & 2021 & Network Traffic & KNN, SVM, DT, NB, RF, and ANN \\ [PERSON] et al. [(20)] & 2022 & Finance & MLP, RF, NB, LGBM, Ada Boost, KNN, LR, and MLP \\ [PERSON] et al. [(36)] & 2021 & Logistics & SVR \\ [PERSON] et al. [(5)] & 2022 & Medical diagnosis & LR, MLP, RF, and DT \\ [PERSON] et al. [(16)] & 2022 & Online Game & MLP \\ [PERSON] et al. [(7)] & 2022 & Medical diagnosis & MLP \\ [PERSON] et al. [(19)] & 2022 & Finance & MLP \\ [PERSON] and [PERSON] [(31)] & 2022 & Logistics & Ant colony \\ [PERSON] [(33)] & 2022 & Logistics & CNN-GRU \\ [PERSON] and [PERSON] [(34)] & 2022 & Logistics & 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the related works.  
As demonstrated in the preceding figure, the initial phase involves the acquisition of data from diverse stakeholders. In the subsequent step, the data undergo preprocessing, involving transformations and standardization, to optimize its format for model learning. Following preprocessing, the data are input into a neural network to construct a model, necessitating decisions on the architecture including the number of layers, neural units, initialization functions, and activation functions for each layer. Additionally, specifications for the overall model, such as the choice of loss function, optimizer, and metrics, are determined. Concurrently, the preprocessed data are utilized in machine learning models for a comparative analysis of results with those obtained from the MLP. Hence, we present four ML models and the MLP network in this study.