用扩散模型自动生成门店货架布局，大幅降低设计时间和成本。
Cloud-Native Generative AI for Automated Planogram Synthesis: A Diffusion Model Approach for Multi-Store Retail Optimization
- 基于扩散模型学习多店成功布局，生成新方案
- 设计时间从30小时降至0.5小时，约束满足率达94.4%
- 适合需要快速优化零售陈列的电商与连锁品牌
货架布局规划是零售业的重大挑战，复杂布局平均需耗时30小时。本文提出一种云原生架构，采用扩散模型实现门店专属布局的自动化生成。不同于传统重排现有布局的优化方法，本系统通过学习多个零售点的成功陈列，生成全新配置。架构结合AWS云端训练与边缘端实时推理，利用改进的损失函数融入零售特定约束。仿真分析显示，设计时间减少98.3%（从30小时降至0.5小时），约束满足率达94.4%。经济分析表明，创建成本降低97.5%，投资回收期为4.4个月。云原生架构可线性扩展，支持最多10,000个门店并发请求。本工作验证了生成式AI在自动化零售空间优化中的可行性。
原文摘要 · Abstract (English)
Planogram creation is a significant challenge for retail, requiring an average of 30 hours per complex layout. This paper introduces a cloud-native architecture using diffusion models to automatically generate store-specific planograms. Unlike conventional optimization methods that reorganize existing layouts, our system learns from successful shelf arrangements across multiple retail locations to create new planogram configurations. The architecture combines cloud-based model training via AWS with edge deployment for real-time inference. The diffusion model integrates retail-specific constraints through a modified loss function. Simulation-based analysis demonstrates the system reduces planogram design time by 98.3% (from 30 to 0.5 hours) while achieving 94.4% constraint satisfaction. Economic analysis reveals a 97.5% reduction in creation expenses with a 4.4-month break-even period. The cloud-native architecture scales linearly, supporting up to 10,000 concurrent store requests. This work demonstrates the viability of generative AI for automated retail space optimization.
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