Aiming to address the dual challenges of multi-objective optimization and complex demand forecasting in logistics inventory management, this study proposes an intelligent decision-making system that integrates a multi-objective flora optimization algorithm and a dense attention network. By introducing an improved bacterial foraging optimization algorithm, the three-objective collaborative optimization of inventory cost, out-of-stock risk, and service level is achieved, and a deep learning model is constructed by combining a dense attention mechanism to enhance the accuracy of demand forecasting and dynamic adjustment ability. Experimental data show that the system has verified remarkable effects in real logistics scenarios: the total inventory cost is reduced by 23.6%, the out-of-stock rate is reduced by 18.2%, and the prediction accuracy is increased by 15.8%; Under the scale of 100,000 SKUs, the decision response time has been shortened to 3.2 seconds, which is 40% faster than the traditional method. Through the actual measurement of a specific large-scale e-commerce platform, the annual turnover rate increased by 28%, storage space utilization rose by 19%, and the order satisfaction rate reached 98.7%; it should be noted that these results are case-specific, and their external validity in diverse logistics environments requires further verification. The innovation of this research lies in combining biological heuristic optimization with deep learning to build a two-tier decision-making architecture. A dynamic weight allocation mechanism is proposed to achieve an adaptive balance among multiple objectives. The attention mechanism is introduced to strengthen the extraction of key features and improve the robustness of the prediction model. Numerical experiments and practical cases demonstrate the effectiveness and scalability of the system in a complex logistics environment, providing a new theoretical method and practical approach for intelligent inventory management.
Fangfang Liu (Fri,) studied this question.