Integrating incentive-driven delivery options and time-dependent routing decisions with availability profiles

As e-commerce continues to evolve, the issue of first delivery failures due to customers not being at home has become increasingly prominent, significantly increasing the operational costs of logistics companies. This paper considers an incentive-driven (ID) strategy, whereby logistics companies offer cash rewards to encourage customers to choose a designated delivery location instead of home delivery. However, there is uncertainty regarding whether customers will accept this incentive. We model this problem as a two-stage stochastic programming problem with incentive uncertainty. Furthermore, considering the impact of urban traffic conditions on delivery efficiency, this paper embeds the ID strategy into a time-dependent routing decision problem with customer-individual availability profiles (APs), where the APs consist of a set of time windows (TWs). Each TW is associated with both the cost of delivery failure and the probability of successful delivery. We refer to this integrated decision problem as the incentive-driven delivery options and time-dependent vehicle routing problem with customer-individual APs (IDDO-TDVRPAP). To effectively solve this problem, valid inequality is introduced to enhance the performance of solving IDDO-TDVRPAP. Moreover, due to the limited performance of solvers in handling large-scale instances, we propose a novel hybrid solution-based tabu search with self-adaptive local search (HSBTS-SALS) and evaluate it on generated test instances. Finally, we summarize and discuss the experimental results. The research results indicate that the integration of the ID strategy and APs offers new methods for addressing last-mile delivery challenges, significantly enhancing customers’ perception of service quality.