Big Data Swarm Intelligence Optimization Algorithm Application in the Intelligent Management of an E-Commerce Logistics Warehouse

Introduction

According to available data, the e-commerce logistics industry has experienced significant growth in recent years. As of 2022, the global e-commerce logistics market reached a value of $3.51 trillion and is projected to continue growing steadily in the foreseeable future (Zhao et al., 2020). Traditional logistics systems are no longer capable of meeting the increasing demands of e-commerce logistics, particularly in terms of efficient goods planning and convenient accessibility. Moreover, traditional technologies face numerous challenges in equipment integration and optimizing goods management, resulting in operational issues such as slow delivery and inefficient warehouse paths (Pan et al., 2022). Furthermore, although traditional logistics management has become increasingly intelligent, there is still a lack of effective management and scheduling mechanisms, including path selection and space allocation, which significantly impact the management of logistics warehouse goods. To address these issues, a highly efficient logistics warehouse operation and management model based on big data technology is proposed. This model introduces an innovative population-based intelligent optimization model to achieve efficient management of logistics warehouse operations (Pu et al., 2022). Specifically, the proposed model presents a Multi-Objective Particle Swarm Optimization (MOPSO) approach for optimizing warehouse allocation, aiming to tackle the problem of slow cargo allocation within warehouses. Additionally, a dynamic mutation probability formula is introduced to optimize traditional models that frequently converge towards local optima. To enhance the efficiency of logistics warehouse paths, the study employs Ant Colony Optimization algorithms based on genetic algorithms (GA-ACO) to develop a logistics warehouse path optimization model, thereby achieving optimized logistics warehouse paths. The research content includes four parts. The first part introduces the current situation of e-commerce logistics warehouse management and the specific application of related technologies. At the same time, it studies and discusses the multi-objective optimization technology of e-commerce. The second part studies the e-commerce logistics warehouse system and related technologies, analyzes the optimization objectives of e-commerce management, and constructs two optimization models for e-commerce logistics warehouse management. The third part is to apply the mentioned technology to specific scenarios and verify the optimization effect of the proposed logistics warehouse management model in actual scenarios. The fourth part summarizes and analyzes the entire article, and elaborates on the improvement direction of the research.