Research on Green Warehousing Logistics Site Selection Optimization and Path Planning based on Deep Learning

When it comes to e-commerce and logistics industry developing rapidly, the location and path planning of green warehousing and logistics have attracted more and more attention, and how to improve efficiency and reduce environmental impact has become the focus of research. This paper presents a new optimization approach based on Deep Neural Network (DNN), which is used to solve the multi-objective optimization problem in the warehouse location and path planning. We propose to use a deep neural network model to optimize the weights and parameters of the network as a whole through the combined PSO method, so that the dynamic and diverse needs of the logistics environment can be better adapted to changes. Unlike traditional methods based on heuristics or simple machine learning models, this paper adopts the principle of minimizing the transportation cost, while achieving the minimization of energy consumption and the limited carbon emissions in path planning, i.e. optimizing the particle swarm of DNN. Empirical results indicate that, when compared to classical optimization techniques, our approach enhances transportation cost and environmental sustainability by 18% and 12%.