Assortment planning, integral to multiple commercial offerings, is a key problem studied in e-commerce and retail settings. Numerous variants of the problem along with their integration into business solutions have been thoroughly investigated in the existing literature. However, the nuanced complexities of in-store planning and a lack of optimization proficiency among store planners with strong domain expertise remain largely overlooked. These challenges frequently necessitate collaborative efforts with multiple stakeholders which often lead to prolonged decision-making processes and significant delays. To mitigate these challenges and capitalize on the advancements of Large Language Models (LLMs), we propose an interactive assortment planning framework, InteraSSort that augments LLMs with optimization tools to assist store planners in making decisions through interactive conversations. Specifically, we develop a solution featuring a user-friendly interface that enables users to express their optimization objectives as input text prompts to InteraSSort and receive tailored optimized solutions as output. Our framework extends beyond basic functionality by enabling the inclusion of additional constraints through interactive conversation, facilitating precise and highly customized decision-making. Extensive experiments demonstrate the effectiveness of our framework and potential extensions to a broad range of operations management challenges.

翻译：品类规划作为多项商业供给的核心环节，是电子商务与零售领域研究的重点问题。现有文献已深入探讨了该问题的多种变体及其在商业解决方案中的集成应用。然而，针对实体店规划中存在的细微复杂性，以及具备深厚领域知识的门店规划人员普遍缺乏优化专业知识的问题，仍被广泛忽视。这些挑战常需与多方利益相关者协作，导致决策流程冗长且产生显著延迟。为应对上述挑战并充分利用大型语言模型（LLMs）的进步，我们提出交互式品类规划框架InteraSSort，该框架通过优化工具增强LLM能力，以交互式对话辅助门店规划人员进行决策。具体而言，我们开发了一套包含用户友好界面的解决方案，使用户能够以文本提示形式向InteraSSort输入优化目标，并输出定制化优化方案。本框架突破基础功能局限，通过交互式会话支持附加约束条件的动态集成，从而实现精准且高度定制化的决策。大量实验验证了该框架的有效性，及其在更广泛的运营管理挑战中的潜在扩展能力。