Revolutionizing Automotive Manufacturing and Distribution: Leveraging AI and Cloud Computing for Smart Supply Chains

Abstract

Revolutionizing automotive manufacturing and subsequent distribution has been a growing debate for the industry due to electric cars, artificial intelligence, and cloud computing. Leveraging AI models for predicting passenger demand and enhancing the energy consumption of both the vehicles and warehouses are discussed. Cloud computing solutions for electrical vehicle charging station placement are surveyed and a fleet management algorithm is proposed that aims at satisfying EV driver/utility providers preferences. A multi-agent system is developed in an environment. The bridge between AI and logistics is built through an autonomous vehicle algorithm that can distribute the passengers using our predictive model about passenger demand for different areas at different times. The algorithm is applied to different solutions of the capacitated vehicle routing problem for reducing the costs of delivering the electric cars. Also, a reinforcement learning algorithm is proposed that jointly enhances the energy consumption and the robot path planning. The algorithm is used to develop a deep deterministic policy gradient agent as an assembler, and the agent is applied to the AGV scheduling optimization where the AGVs can select different feasible and safe paths across the robot cell with different maintenance tasks and obstacles. Cloud computing is delivered through the internet. Data collection is provided by the IoT devices in the robot cell while data storage is provided by a service. Despite some electrical disparities between the cloud computing and IoT devices, data processing and information output are shown to have a complementary integration.