  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.
