将预测与优化结合，提升纺织和防护用品供应链的精准度与效率。
Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization
- 用LSTM预测需求，再通过数学规划优化补货分配。
- MAE降低14.7%，库存成本降5.4%，缺货率下降27.5%。
- 适合需要高响应速度的制造与应急供应链场景。
在需求波动大、供应不确定的行业（如纺织品和个人防护装备）中，供应链韧性与效率至关重要。传统预测与优化方法常独立运行，限制了实际效果。本文提出一种混合AI框架HAF-DS，将基于LSTM的需求预测模块与混合整数线性规划（MILP）优化层结合。LSTM捕捉需求的时间与上下文依赖关系，优化层则给出成本最低的补货与分配策略。通过嵌入特征表示与循环神经网络，联合最小化预测误差与运营成本。在纺织品销售与供应链数据集上的实验表明，相比统计与深度学习基线，该框架显著提升性能：在综合数据集上，平均绝对误差（MAE）从15.04降至12.83（降低14.7%），均方根误差（RMSE）从19.53降至17.11（降低12.4%），平均绝对百分比误差（MAPE）从9.5%降至8.1%。库存成本降低5.4%，缺货率下降27.5%，服务水平由95.5%提升至97.8%。结果验证了预测与优化耦合可同时提升准确率与效率，为现代纺织与防护用品供应链提供可扩展、可适应的解决方案。
原文摘要 · Abstract (English)
Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal protective equipment (PPE). Traditional forecasting and optimization approaches often operate in isolation, limiting their real-world effectiveness. This paper proposes a Hybrid AI Framework for Demand-Supply Forecasting and Optimization (HAF-DS), which integrates a Long Short-Term Memory (LSTM)-based demand forecasting module with a mixed integer linear programming (MILP) optimization layer. The LSTM captures temporal and contextual demand dependencies, while the optimization layer prescribes cost-efficient replenishment and allocation decisions. The framework jointly minimizes forecasting error and operational cost through embedding-based feature representation and recurrent neural architectures. Experiments on textile sales and supply chain datasets show significant performance gains over statistical and deep learning baselines. On the combined dataset, HAF-DS reduced Mean Absolute Error (MAE) from 15.04 to 12.83 (14.7%), Root Mean Squared Error (RMSE) from 19.53 to 17.11 (12.4%), and Mean Absolute Percentage Error (MAPE) from 9.5% to 8.1%. Inventory cost decreased by 5.4%, stockouts by 27.5%, and service level rose from 95.5% to 97.8%. These results confirm that coupling predictive forecasting with prescriptive optimization enhances both accuracy and efficiency, providing a scalable and adaptable solution for modern textile and PPE supply chains.
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