The two-echelon vehicle routing problem with pickups, deliveries, and deadlines

This paper introduces the Two-Echelon Vehicle Routing Problem with Pickups, Deliveries, and Deadlines (2E-VRP-PDD), an emerging routing variant addressing the operations of logistics companies connecting consumers and suppliers in metropolitan areas. Logistics companies typically organize their logistics in such metropolitan areas via multiple geographically dispersed two-echelon distribution systems. The 2E-VRP-PDD is the practical problem that needs to be solved within each of such a single two-echelon distribution system, thereby merging first and last-mile logistics operations. Specifically, it integrates the distribution of last-mile parcels from the hub via satellites to the consumers with the collection of first-mile parcels from the suppliers via satellites that return to the hub. Moreover, it considers deadlines before first-mile parcels arrive at the hub, which must be transported further in the network. We solve the 2E-VRP-PDD with a newly developed Adaptive Large Neighborhood Search (ALNS) combined with a post-process integer programming model. Our ALNS provides high-quality solutions on established benchmark instances from the literature. On a new benchmark set for the 2E-VRP-PDD, we find that modifying time restrictions, such as parcel delivery deadlines at the city hub, can lead to an 8.27% cost increase, highlighting the overhead associated with same-day delivery compared to next-day delivery operations. Finally, by analyzing real-life instances containing up to 2150 customers obtained from our industry collaborator in Jakarta, Indonesia, we show that our ALNS can reduce the cost of operations by up to 17.54% compared to current practice. • We study first- and last-mile city logistics in metropolitan areas. • We introduce Two-Echelon Vehicle Routing Problems with Pickup, Delivery, and Deadlines. • We propose a method combining ALNS and a set partitioning model. • In a real-life case study in Jakarta, Indonesia, we show up to 17.5% cost savings. • Our set partitioning model enhances solution quality by 0.39%