Last-mile delivery problem with flexible time slot and location options under stochastic customer behavior
研究最后一英里配送中，顾客对时段和地点偏好不确定时，如何通过混合配送策略和定价决策优化配送方案，并设计了高效启发式算法求解。
The growth of e-commerce has led to a significant rise in daily parcel deliveries, placing increasing pressure on logistics services. Although various delivery options, including home and out-of-home delivery, are offered to enhance customer availability, uncertainty in customer preferences regarding delivery locations and time slots further complicates delivery operations. In this paper, we introduce a last-mile delivery problem with flexible delivery options under stochastic customer behavior, formulated as a two-stage stochastic programming model. The novelty of our model lies in the integration of comprehensive delivery options, including various locations and time slots, with pricing decisions. Furthermore, we incorporate heterogeneous customer preferences across mixed delivery options. To efficiently solve the problem, we develop a hybrid adaptive large neighborhood search (h-ALNS) heuristic based on scenario simulations. Extensive numerical experiments demonstrate the advantages of the mixed delivery strategy and confirm the effectiveness of the proposed algorithm. Our numerical analysis offers important managerial insights for optimizing last-mile delivery assortment and pricing, particularly in addressing customer uncertainty.