Robust Assortment Planning With Operational Considerations for E‐commerce Retailers: A Three‐Phase Solution Framework

ABSTRACT With the rapid expansion of e‐commerce marketplace, major e‐tailers are implementing a two‐layer distribution network with regional and forward distribution centers (FDCs) for timely delivery. We study an e‐commerce assortment planning problem that incorporates operational considerations over a multi‐period horizon. Before the horizon starts, an e‐tailer determines the assortment presented online and a partial assortment stored at each FDC. In each period, she first replenishes products from the supplier, then allocates inventory to FDCs, finally fulfills realized product demands adaptively, with an objective of maximizing the expected total profit. Given the distributional ambiguity in practice, we formulate a distributionally robust optimization model based on customers' multinomial logit choices, which presents the challenges of fractional nonlinearity and computational complexity. To address them, we propose a tractable three‐phase solution framework that integrates the strengths of conic programming, linear decision rule, and the exact branch‐and‐Benders‐cut algorithm. Numerical experiments suggest its good computational performance from various aspects, and explore the values of incorporating operational considerations and assortment selection. A case study using real data from JD.com demonstrates the practical applicability of our framework, which increases profit by 102.6% over JD.com's status quo policy. The profit increment consistently increases over time, suggesting the significant potential long‐term benefits from implementing our framework.