An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits
研究了最后一英里配送中客户不在家时的二次访问问题，提出自适应大邻域搜索算法，在提高签收率和降低运输成本间取得平衡，可节省高达32%的成本。
Attended Home Delivery, where customer attendance at home is required, is an essential last-mile delivery challenge, e.g., for valuable, perishable, or oversized items. Logistics service providers are often faced no-show customers. In this paper, we consider the delivery problem in which customers can be revisited on the same day by a courier in the case of a failed first delivery attempt. Specifically, customer presence uncertainty is considered in a two-stage stochastic program, where penalties are introduced as recourse actions for failed deliveries. We build on the notion of a customer availability profile defined as a profile containing historical time-varying probability information of successful deliveries. We tackle this stochastic program by developing an efficient parallelized Adaptive Large Neighborhood Search algorithm. Our results show that by achieving a right balance between increasing the hit rate and reducing travel cost, logistics service providers can realize costs savings as high as 32% if they plan for second visits on the same day.