A Re-Solving Heuristic for Dynamic Assortment Optimization With Knapsack Constraints
针对资源背包约束下的多阶段品类优化问题，提出一种基于epoch的重求解算法，将非线性流体近似转化为线性规划，并证明遗憾值随时间长度和资源容量的对数增长。
In this article, we consider a multi-stage assortment optimization problem with multinomial logit (MNL) choice modeling under resource knapsack constraints. Given the current resource inventory levels, the retailer makes an assortment decision at each period, and the goal of the retailer is to maximize the total profit from purchases. With the exact optimal dynamic assortment solution being computationally intractable, a practical strategy is to adopt the re-solving technique that periodically re-optimizes deterministic linear programs (LPs) arising from fluid approximation. However, the fractional structure of MNL makes the fluid approximation in assortment optimization non-linear, which brings new technical challenges. To address this challenge, we propose a new epoch-based re-solving algorithm that effectively transforms the denominator of the objective into the constraint, so that the re-solving technique is applied to a linear program with additional slack variables amenable to practical computations and theoretical analysis. Theoretically, we prove that the regret (i.e., the gap between the re-solving policy and the optimal objective of the fluid approximation) scales logarithmically with the length of time horizon and resource capacities.