用深度学习预测咖啡需求，联合优化成本、排放与新鲜度。
Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management
- 用混合CNN-LSTM模型预测需求，比最优基准提升12%。
- 三目标优化使碳排放降22.4%，成本仅增9.9%，保鲜接近最优。
- 适合关注可持续供应链与智能决策的研究者和企业
咖啡供应链是复杂的农业食品网络，具有地理分散、多层级协同及对品质和新鲜度高度敏感的特点。本文提出两阶段集成框架：首先在公开的咖啡链销售数据集（时间序列70/15/15划分）上，采用混合CNN-LSTM模型进行需求预测，实现MAE为22.87，R²为0.90，优于最佳深度学习基准约12%，经典方法超30%。第二阶段将预测需求输入三目标混合整数线性规划（MILP）模型，同时最小化成本、碳排放，最大化产品新鲜度，在多周期、多模式、闭环回收的循环供应链中实现。新鲜度基于库存龄指数衰减建模。通过epsilon约束法获得25个帕累托解。敏感性和政策分析表明，均衡可持续政策可降低22.4%碳排放，成本仅上升9.9%，且保持近最优新鲜度。
原文摘要 · Abstract (English)
The coffee supply chain is one of the most complex agri-food networks, marked by geographically dispersed production, multi-tier coordination, and high sensitivity to quality and freshness. While sustainability and digitalization have gained attention, demand forecasting, optimization, and traceability are often treated separately. This study presents a two-phase integrated framework. First, a hybrid CNN-LSTM model is used for demand forecasting. On the public Coffee Chain Sales dataset with chronological 70/15/15 splitting, the model achieves MAE of 22.87 and R^2 of 0.90, outperforming the best deep learning benchmark by ~12% and classical methods by over 30%. In the second phase, the forecasted demand feeds a tri-objective mixed-integer linear programming (MILP) model that jointly minimizes cost, minimizes carbon emissions, and maximizes product freshness in a multi-period, multimodal, closed-loop supply chain with circular recovery. Freshness is modeled via exponential decay based on inventory age. Using the epsilon-constraint method, 25 Pareto solutions are obtained. Sensitivity and policy analyses show that balanced sustainability policies can reduce emissions by 22.4% with only a 9.9% cost increase while maintaining near-optimal freshness. Keywords: Coffee supply chain; Deep learning; Demand forecasting; Multi-objective optimization; Circular economy; CNN-LSTM; Mixed-integer linear programming.
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