# Last-Mile Optimization Using Neural Networks
## 1 Introduction  
Manufacturing organizations need to ensure the optimization of their supply chain and urban logistics operations in order to help manufacturers reduce their transportation costs; avoid congestion and delays; improve their supply chain performance; and, therefore, contribute to improving manufacturing processes by adopting sustainable practices and achieving their goals effectively. The industrial supply chain includes the last-mile transportation process, which represents 20% of the overall supply chain cost, as shown in Figure 1. This phase often presents challenges such as cost management, delivery delays, urban congestion, and route optimization. Urban logistics have a significant impact on the economy, urban growth, and social and environmental problems, involving various stakeholders such as shippers (manufacturers, wholesalers, and retailers) and carriers (shipping companies, warehouse companies), managers (national, state, provincial, and city levels), and residents [1]. Moreover, urban logistics is the art of efficiently transporting flows of products to urban areas. The products involved are of all kinds and concern not only businesses that produce products or services but also retailers, construction sites, and deliveries made by residents of the urban area. The analysis of urban logistics is therefore very complex as it involves many actors including transporters, businesses, and local authorities. Adding to that is the growingavailability and volume of data in the manufacturing industry, which have drawn attention to the study of machine learning (ML) concepts that can help to effectively process and analyze the data. ML and Deep Learning (DL) techniques have been used in the manufacturing sector. In fact, classification ML algorithms can be used in urban logistics to categorize different types of products and make decisions about their transportation, delivery, and categorization. These intelligent systems have the potential to support effective decision-making and optimize manufacturing processes. ML stands as a distinct domain within Artificial Intelligence (AI), focusing on the creation of algorithms and statistical models. These models enable computers to learn from experiences, facilitating accurate predictions of future events. ML broadly falls into two categories: supervised learning and unsupervised learning. Its versatile applications span across various fields; for instance, the categorization of objects into predefined groups, a well-explored aspect in ML, is widely employed in manufacturing. The ML field, containing DL, which specifically focuses on neural networks, has shown great potential for improving fields classification. There are a variety of fields applications, including health, education, wireless sensor networks, finance, natural language processing, computer vision, recommendation systems, fraud detection, and medical diagnosis. The classification of last-mile flows is a crucial step allowing for efficient mutualization of the delivery, efficient planning and scheduling, and a financial gain. For this purpose, the use of MLP neural networks will be able to meet and guarantee these objectives. This can reduce operational costs, increase efficiency, and speed up delivery times, which is essential in a growing e-commerce environment. More specifically, the main contributions of this paper are as follows:  
* Development of an ensemble classification approach using multiple ML models and MLP models as well as automation of last-mile logistics processes. * Improvement of delivery times and supply chain sustainability by optimizing products flows in the city. The paper emphasizes the significance of classifying last-mile flows for mutualizing delivery, efficient planning, scheduling, and financial gains. MLP neural networks play a crucial role in meeting these objectives, reducing operational costs, improving efficiency, and accelerating delivery times. Besides, the paper includes the development of an en  
Figure 1: Supply chain [2].