Diffusion-Based Data-Driven Assortment Optimization

Researchers at arXiv introduced a model-agnostic framework for assortment optimization using guided discrete diffusion, representing assortments as binary vectors and employing a reward-guided reverse diffusion process to balance exploration and exploitation. The method consistently identifies high-quality assortments, remains robust under model misspecification, and recovers near-optimal solutions in high-dimensional settings, highlighting generative modeling's potential for scalable combinatorial optimization.

arXiv:2608.11419v1 Announce Type: new Abstract: 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.