【日期】11月9日

【时间】14:00—15:00

【主题】Taylor Approximation of Inventory Policies for One-Warehouse, Multi-Retailer Systems with Demand Feature Information

【主讲人】Prof. Yi Yang（杨翼）

【主持人】Prof. Tianhu Deng（邓天虎）

【语言】中文

【参加方式】腾讯会议Tencent Meeting，会议ID：328 742 032

【讲座介绍】Motivated by Fresh Hema, we consider a distribution system in which retailers replenish perishable goods from a warehouse, which, in turn, replenishes from an outside source. Demand at each retailer depends on exogenous features and a random shock, and unfulfilled demand is lost. The objective is to obtain a data-driven replenishment and allocation policy that minimizes the average inventory cost per time period. Because of the demand features and the allocation constraint, solving the problem with the extent data-driven methods either possibly violates the constraint or is subject to the curse of dimensionality. We construct a data-driven policy that resolves these issues in two steps. In the first step, we assume that the distributions of features and random shocks are known. We develop an effective heuristic policy by using Taylor expansion to approximate the retailer's inventory cost. The resulting solution yields a closed-form one referred to as Taylor Approximation (TA) policy. We show that the TA policy is asymptotically optimal in the number of retailers. In the second step, we apply the linear quantile regression, kernel density estimation, and sample average approach to the TA solution to obtain the data-driven policy referred to as Data-Driven Taylor Approximation (DDTA). We prove that the DDTA policy is consistent with the TA policy. A numerical study shows that our DDTA policy outperforms the other data-driven policies in the literature. Using a real data set provided by Fresh Hema, we show that the DDTA policy reduces the average cost by 11\%, compared to the current policy. Finally, we show that the main results still hold under some mild conditions in the cases of correlated demand features between either periods or retailers, the censored demand, and positive lead time.

【主讲人介绍】杨翼，浙江大学管理学院副院长，浙江大学长聘教授，求是特聘教授，博士生导师，数据驱动决策研究所所长，物流与决策优化研究所所长，运筹学会青年科技奖获得者。2011年毕业于香港中文大学系统工程与工程管理学系。主要研究方向包括供应链管理、运营管理、数据驱动决策。在高水平国际期刊上发表论文十余篇，特别是在 《Management Science》，《Operations Research》,《Manufacturing & Service Operations Management》,《Production and Operations Management》等经济管理类国际公认顶级期刊上发表学术论文多篇。承担多项国家及省部级课题，现担任《Naval Research Logistics》、JORSC、APJOR的Associate Editor，《管理工程学报》领域编委，《运筹与管理》编委；担任运筹学会随机服务与运作管理分会理事、系统工程学会物流系统工程专业委员会理事、运筹学会智能工业数据解析与优化分会理事等工作。