报告时间：2022年12月14日（星期三）下午3:00-4:30
报告地点：线上报告（腾讯会议：749 622 998）
报告人：李晓波
工作单位：新加坡国立大学
举办单位：威廉希尔
报告简介：
This paper studies the single-warehouse assortment selection problem that aims to minimize the order fulfillment cost under the cardinality constraint. We propose two types of fulfillment-related cost functions, which correspond to different preferences toward spillover fulfillment and order-splitting. This problem includes the fill rate maximization problem as a special case. First, we show that the objective function is submodular for a broad class of cost functions. Second, we show that even the fill rate maximization problem with the largest order size being two is NP-hard. Next, we propose a simple heuristic called the marginal choice indexing (MCI) policy, which stores the most popular products. We find a general condition under which the MCI policy is optimal, and this condition can be satisfied by all classic discrete choice models and several multi-purchase choice models. Additionally, we demonstrate by synthetic experiments that the MCI policy is robust when the actual demand distribution is not obtainable. Furthermore, we propose an enhanced mixed integer linear programming (MILP) formulation with the easy-to-implement Benders decomposition scheme. Through extensive numerical experiments on a real-world dataset from RiRiShun Logistics, we find that the MCI policy is surprisingly near-optimal in all the settings we tested. Simply applying the MCI policy, the fill rate is estimated to improve by 9.18% on average compared to the current practice for the local transfer centers (LTCs) on the training data set. More surprisingly, the MCI policy outperforms the optimal policy in 14 out of 25 cases on the test data set. This demonstrates that the MCI policy is robust to the change of demand function since it only requires knowledge of the marginal choice probability.
报告人简介：
Xiaobo Li is an assistant professor in the Department of Industrial Systems Engineering and Management at the National University of Singapore. He received his Ph.D. in Industrial Engineering from the University of Minnesota in 2018. His research mainly focuses on robust optimization, discrete choice modelling and dynamic programming, with applications in revenue management, data-driven decision making and supply chain management.
