云原生生成式AI用于自动计划图合成：一种基于扩散模型的多店零售优化方法
Cloud-Native Generative AI for Automated Planogram Synthesis: A Diffusion Model Approach for Multi-Store Retail Optimization
AI总结：
本文提出一种基于扩散模型的云原生架构，用于自动优化多店零售计划图生成，显著降低设计时间和成本。
摘要：
计划图创建是零售业的重大挑战，每个复杂布局需要平均30小时。本文介绍了一种使用扩散模型的云原生架构，用于自动生成针对商店的计划图。与传统优化方法不同，我们的系统从多个零售地点的成功货架排列中学习，以创建新的计划图配置。该架构结合了通过AWS进行的云基础模型训练与边缘部署的实时推理。扩散模型通过修改的损失函数整合零售特定的约束。基于仿真的分析显示，该系统将计划图设计时间减少了98.3%（从30小时到0.5小时），同时实现了94.4%的约束满足。经济分析显示，创建成本减少了97.5%，折损期为4.4个月。云原生架构可线性扩展，支持高达10,000个并发商店请求。本工作展示了生成式AI在自动零售空间优化中的可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
