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.

翻译：货架图创建是零售业面临的一项重大挑战，每个复杂布局平均需要30小时。本文提出一种采用扩散模型的云原生架构，用于自动生成针对特定门店的货架图。与重组现有布局的传统优化方法不同，本系统通过从多个零售门店的成功货架陈列中学习，生成全新的货架图配置方案。该架构结合了基于AWS的云端模型训练与支持实时推理的边缘部署。扩散模型通过改进的损失函数整合了零售领域的特定约束条件。基于仿真的分析表明，该系统将货架图设计时间减少98.3%（从30小时降至0.5小时），同时实现94.4%的约束满足率。经济分析显示创建成本降低97.5%，投资回收期为4.4个月。云原生架构支持线性扩展，可同时处理多达10,000个门店请求。本研究证明了生成式AI在零售空间自动化优化领域的可行性。