Dynamic stochastic parcel locker assignment with uncertain pick-up times
研究物流公司在动态接收订单时，如何立即决定是否接受，并随后将订单分配到包裹柜，同时考虑未来订单到达和客户取件时间的不确定性，提出基于分类的决策框架，在计算实验中优于多种基准方法。
Automated parcel lockers are used by logistics providers in order to increase the efficiency of last-mile delivery operations particularly in urban areas. We consider the case of a company that operates lockers and dynamically receives delivery orders that have to be accepted or rejected immediately. On a second decision stage, the set of accepted orders has be assigned to the lockers such that customer compatibility requirements and maximum fulfillment times are respected. As both future arrivals of orders as well as customer pickup-times are unknown, the company faces a dynamic stochastic problem. To generate solutions, we propose a decision framework based on a classification approach. The classifier uses a mixed integer model to learn from optimal solutions within a deterministic setting and exploits this information within the dynamic stochastic process. We assess the proposed method within an extensive computational study where both artificial instances and a real world case are addressed. The obtained results show that the classification-based framework outperforms all benchmark methods, which include (i) scenario sampling, (ii) classical decision trees, and (iii) several deterministic policies. Managerial insights with regard to most important systems’ features within the decision process are derived. The newly proposed decision framework is generalizable such that it can be applied to related dynamic stochastic matching problems. • We introduce and formalize the Dynamic Stochastic Parcel Locker assignment with uncertain pick-up times (DSPLAU). • We propose an Oracle model which provides an upper bound to the problem. • We provide a new solution method based on an IP-based classifier. • We strongly outperform standard classifiers such as Decision Trees and Logistic Regression. • We provide managerial insights.