Bayesian Optimization of LSTM-Driven Cold Chain Warehouse Demand Forecasting Application and Optimization

With the gradual adoption of smart hardware such as the Internet of Things (IoT) in warehousing and logistics, the efficiency bottlenecks and resource wastage inherent in traditional storage management models are now poised for breakthrough through digital and intelligent transformation. This study focuses on the cross-border cold chain storage scenario for Malaysia’s Musang King durians. Influenced by the fruit’s extremely short 3–5-day shelf life and the concentrated harvesting period in primary production areas, the issue of delayed dynamic demand response is particularly acute. Utilizing actual sales order data for Mao Shan Wang durians from Beigang Logistics in Guangxi, this study constructs a demand forecasting model integrating Bayesian optimization with bidirectional long short-term memory networks (BO-BiLSTM). This aims to achieve precise forecasting and optimization of cold chain storage inventory. Experimental results demonstrate that the BO-BiLSTM model achieved an R2 of 0.6937 on the test set, with the RMSE reduced to 19.1841. This represents significant improvement over the baseline LSTM model (R2 = 0.5630, RMSE = 22.9127). The bidirectional Bayesian optimization mechanism effectively enhances model stability. This study provides a solution for forecasting inventory demand of fresh durians in cold chain storage, offering technical support for optimizing the operation of backbone hub cold storage facilities along the New Western Land–Sea Trade Corridor.