用注意力LSTM+粒子群优化，实现生鲜超市动态定价与补货
Integrating Attention-Enhanced LSTM and Particle Swarm Optimization for Dynamic Pricing and Replenishment Strategies in Fresh Food Supermarkets
- 用带注意力机制的LSTM预测7天销量、价格趋势和变质率
- 粒子群算法优化策略，使利润最大化且减少浪费
- 可解释性强，适合需要实时响应的生鲜零售场景
本文提出一种融合长短期记忆网络（LSTM）与粒子群优化（PSO）的新方法，用于优化生鲜超市的定价与补货策略。增强注意力机制的LSTM模型用于预测未来7天内的销售量、价格趋势及变质率，其输出作为PSO算法的输入，通过迭代优化定价与补货方案，在满足库存约束的前提下最大化利润。结合成本加成定价机制，实现基于固定与可变成本的动态调整，提升对市场波动的实时响应能力。该框架不仅提高盈利能力，还有效降低食品浪费，推动更可持续的运营。注意力机制增强了LSTM的可解释性，识别出影响销售的关键时间节点与因素，提升决策精度。该方法实现了预测与优化的闭环整合，为生鲜零售及其他易腐商品行业提供可扩展的动态管理解决方案。
原文摘要 · Abstract (English)
This paper presents a novel approach to optimizing pricing and replenishment strategies in fresh food supermarkets by combining Long Short-Term Memory (LSTM) networks with Particle Swarm Optimization (PSO). The LSTM model, enhanced with an attention mechanism, is used to predict sales volumes, pricing trends, and spoilage rates over a seven-day period. The predictions generated by the LSTM model serve as inputs for the PSO algorithm, which iteratively optimizes pricing and replenishment strategies to maximize profitability while adhering to inventory constraints. The integration of cost-plus pricing allows for dynamic adjustments based on fixed and variable costs, ensuring real-time adaptability to market fluctuations. The framework not only maximizes profits but also reduces food waste, contributing to more sustainable supermarket operations. The attention mechanism enhances the interpretability of the LSTM model by identifying key time points and factors influencing sales, improving decision-making accuracy. This methodology bridges the gap between predictive modeling and optimization, offering a scalable solution for dynamic pricing and inventory management in fresh food retail and other industries dealing with perishable goods.
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