基于扩散模型的数据驱动 assortment 优化

中文摘要 AI 辅助

assortment 优化是收益管理中的一个基础问题，通常使用多项 logit（MNL）及其变体等参数选择模型来解决。这些模型虽能形成易处理的公式，但其性能对模型设定误差较为敏感，且往往难以捕捉复杂的客户行为。在本文中，我们提出了一种基于引导离散扩散的 assortment 优化模型无关框架。我们将 assortment 表示为二元向量，并通过学习到的反向扩散过程执行随机搜索，避免了显式组合枚举。为融入决策目标，我们引入了一种奖励引导机制，利用预期收益的估计值来偏置局部转移，这使得该方法在生成过程中能有效平衡探索与利用。实证研究表明，所提方法始终能识别高质量的 assortment，且在模型设定误差下保持鲁棒性，在高维场景中常能恢复接近最优的解。此外，扩散的生成特性还能生成多样化的高性能 assortment，提供了超越单一确定性解的灵活性。这些结果凸显了生成建模作为数据驱动决策中组合优化的可扩展且鲁棒范式的潜力。

英文摘要

Assortment optimization is a fundamental problem in revenue management, typically addressed using parametric choice models such as the multinomial logit (MNL) and its variants. While these models enable tractable formulations, their performance is sensitive to model misspecification and often struggles to capture complex customer behavior. In this paper, we propose a model-agnostic framework for assortment optimization based on guided discrete diffusion. We represent assortments as binary vectors and perform stochastic search via a learned reverse diffusion process, avoiding explicit combinatorial enumeration. To incorporate decision objectives, we introduce a reward-guided mechanism that biases local transitions using estimates of expected revenue. This allows the method to effectively balance exploration and exploitation during generation. Empirically, we show that the proposed approach consistently identifies high-quality assortments and remains robust under model misspecification, often recovering near-optimal solutions in high-dimensional settings. Moreover, the generative nature of diffusion enables the production of diverse high-performing assortments, offering flexibility beyond a single deterministic solution. These results highlight the potential of generative modeling as a scalable and robust paradigm for combinatorial optimization in data-driven decision-making.

发表机构

• Duke University（杜克大学）

机构由 AI 辅助整理，请以论文原文为准。