We study joint inventory and assortment optimization under general substitutable choice models. Before a selling horizon, a decision maker chooses initial inventories subject to a capacity constraint on total units stocked, and customers then arrive sequentially and choose among the offered products. We consider two settings, show-all inventory selection, in which every in-stock product must be offered, and dynamic assortment personalization, in which the decision maker observes each customer's type and may adapt the offered assortment to remaining inventory and purchase history. We develop a unified framework linking each setting to a static optimization problem. For show-all inventory selection, a polynomial-time $α$-approximation for cardinality-constrained static assortment optimization yields guarantees of $0.486α$ under deterministic or Poisson arrivals and $0.4328α$ under increasing-failure-rate (IFR) arrivals. For personalization, the same guarantees hold when $α$ is the approximation factor for the joint assortment and customization problem of El Housni and Topaloglu. Our approach combines a fluid relaxation, demand-dependent fractional inventory allocations, bounds on expected truncated demand, and dependent rounding. Notably, our personalized policy fixes one assortment per customer type before the horizon and then offers every in-stock product within it, so it requires no dynamic re-optimization despite competing against the optimal fully adaptive policy. Finally, we construct instances in which optimal show-all revenue approaches only one-half of the fluid bound under deterministic arrivals and $1-w\approx0.4328567$ under IFR arrivals, where $we^w=1$. With an exact static assortment oracle, our IFR guarantee approaches the latter factor arbitrarily closely.

翻译：暂无翻译