The list of notations.

Recently, Live Streaming Selling (LSS) has become increasingly prevalent. Numerous omnichannel retailers are striving to introduce live streaming channel to absorb additional demand. However, it is challenging to investigate robust pricing and inventory strategies that consider the characteristics of omnichannel operations and LSS with uncertain demand. We consider a joint optimization of ordering, replenishment, order fulfillment, and pricing, where customers are sensitive to prices and delivery times. LSS can influence demand and benefit other channels to take free-riding. Furthermore, service level requirements are formulated as joint chance constraints to guarantee adequate performance. The Worst-case Mean Quantile-Deviation (WMQD) is employed to measure risks. The Wasserstein metric is adopted to design the data-driven ambiguity set. Accordingly, a data-driven Distributionally Robust Joint Chance Constrained Programming (DRJCCP) based on WMQD is constructed. Leveraging the dual theory, Conditional Value-at-Risk (CVaR) approximation, and linearization techniques, the developed model can be transformed into tractable formulations, which can be solved by commercial solvers. We further conduct numerical experiments to demonstrate the efficiency and practicality of our developed model. The comparative results reveal that the DRJCCP model based on WMQD has superior out-of-sample performance and is capable of effectively managing uncertainty, thereby ensuring more robust service levels. Furthermore, the sensitivity analyses are performed to verify the effects of some key parameters on the decision-making. The results indicate that introducing live streaming channel is not always profitable for the retailer and increasing the level of LSS effort can enhance free-riding effect without necessarily improving retailer’s profits.近年来，直播带货（Live Streaming Selling, LSS）日益普及。众多全渠道零售商正积极引入直播渠道以挖掘额外市场需求。然而，在需求不确定的场景下，兼顾全渠道运营与直播带货特性，设计稳健的定价与库存策略颇具挑战。本文针对顾客对价格与配送时效敏感的场景，构建了订货、补货、订单履约与定价的联合优化模型。直播带货不仅会影响市场需求，还会使得其他渠道产生搭便车效应。此外，本文将服务水平要求建模为联合机会约束，以保障系统的充足运行性能。本文采用最坏情况均值分位数偏差（Worst-case Mean Quantile-Deviation, WMQD）来度量风险，基于瓦瑟斯坦（Wasserstein）度量构建数据驱动的模糊集。据此，本文构建了基于WMQD的数据驱动分布鲁棒联合机会约束规划（DRJCCP）模型。本文借助对偶理论、条件风险价值（Conditional Value-at-Risk, CVaR）近似方法与线性化技术，将所提模型转化为可求解的规范形式，可通过商业求解器直接求解。随后，本文通过数值实验验证了所提模型的有效性与实用性。对比实验结果表明，基于WMQD的DRJCCP模型具备更优异的样本外表现，能够有效管控不确定性，进而保障更稳健的服务水平。此外，本文开展敏感性分析，以验证部分关键参数对决策结果的影响。研究结果显示，零售商引入直播渠道并非总能提升盈利水平；提升直播带货的投入力度虽可强化搭便车效应，但未必能改善零售商的整体利润。