Solving Assortment Optimization with First-Order Methods and Neural Networks: A Computational Framework and Public Benchmark

Assortment optimization under complex customer choice models and operational constraints is a central challenge in revenue management. This is because its non-linear objective function, coupled with large-scale and discrete decision variables, renders it computationally expensive to solve. Meanwhile, first-order methods like gradient descent have seen widespread adoption for continuous optimization in large-scale AI systems. We propose a computational framework that combines first-order methods and neural networks to efficiently solve assortment optimization. Our framework features straight-through estimators, which enable gradients to flow through discrete variables, and utilizes neural networks to perturb the gradient updates. We theoretically ground our framework by proving that our method is guaranteed to converge to the globally optimal solution for the unconstrained problem under the Multinomial Logit model (MNL). Furthermore, recognizing the need for standardized evaluation in this domain, we develop and release a public benchmark dataset, available at https://github.com/wch444/Assortment-Benchmark. This dataset, comprising several challenging assortment optimization problems, serves both to empirically test our proposed framework and to provide a robust testbed for the wider research community to evaluate novel algorithmic solutions.