Nowadays, couriers are still the main solution to address the "last mile" problem in logistics. They are usually required to record the delivery time of each parcel manually, which is essential for delivery insurances, delivery performance evaluations, and customer available time discovery. Stay points extracted from couriers' trajectories provide a chance to fill the delivery time automatically to ease their burdens. However, it is challenging due to inaccurate delivery locations and various stay scenarios. To this end, we propose the improved Delivery Time Inference (DTInf+), to infer the delivery time of waybills based on couriers' trajectories. Our solution is composed of three steps: 1) Data Pre-processing, which organizes waybills and stay points by delivery trips, 2) Delivery Location Mining, which obtains the delivery location for each address and each Geocoded waybill location by mining historical delivery caused stay points, and 3) Delivery Event-based Matching, which jointly selects the best-matched stay point for waybills at the same delivery location based on Pointer Network-like model SPSelector to infer the delivery time. Extensive experiments and case studies based on real-world datasets from JD Logistics confirm the effectiveness of our approach. Finally, a system powered by DTInf+ is deployed in JD Logistics.

In order to solve the problems of package delivery delay and package loss caused by the sudden increase of package transportation demand during large-scale promotion activities such as 11.11 and 6.18, this paper proposes a parallel logistic network method, aiming at the logistic network of large logistic enterprises, establishes its equivalent virtual logistic network, senses the package transportation demand and network state of the actual logistic network, circularly simulates the operation in advance, finds the delayed and lost packages, analyzes the reasons, and adjust the parameters of network nodes and transportation lines to ensure that packages are delivered on time at low cost. Then the adjusted virtual network parameters are fed back to the actual logistic network, so as to realize the efficient operation of logistic enterprises. A simulation engine ensures that the simulation of 400 million package distribution in 30 days can be completed in half an hour on a personal computer. The application results show that the parallel logistic network reduces the package transportation time by about 10%. The transportation distance is reduced by 7%. Reduce transportation costs by 15%.

Focusing on the dynamic improvement of the underlying service network configuration, this paper aims to address a specific challenge of redesigning a multi-echelon city logistics distribution network. By considering the intra-echelon connection of facilities within the same layer of echelon, we propose a new distribution network design model by reformulating the classical quadratic assignment problem (QAP). To minimize the overall transportation costs, the proposed model jointly optimizes two types of decisions to enable agile distribution with dynamic “shortcuts”: (i) the allocation of warehouses to supply the corresponding distribution centers (DCs), and (ii) the demand coverage decision from distribution centers to delivery stations. Furthermore, a customized branch-and-bound algorithm is developed, where the lower bound is obtained by adopting Gilmore and Lawler lower Bound (GLB) for QAP. We conduct extensive computational experiments, highlighting the significant contribution of GLB-oriented lower bound, to obtain practical solutions; this type of efficient mathematical lower bounds offers a powerful tool for balancing theoretical research ideas with practical and industrial applicability.

In recent years, with the increase of emerging pick-up requests during service, logistics companies have been driven to integrate delivery and pick-up service in a dynamic environment. To provide a balanced and robust approach to cope with delivery requests and emerging pick-up requests, this article aims at considering and modeling a practically useful service principle as preemptive services. To our knowledge, most existing studies assume that the dynamically arriving requests are handled in a non-preemptive processing sequence; that is, once the delivery person is allocated to a task, the process is noninterruptible till it gets completed. In the preemptive service, a service suspension of the delivery process (with low service utility) is allowed to satisfy the pick-up requests (with high service utility) first. To provide a systematic assessment on the value of preemptive service for evolving urban logistics systems, a dynamic vehicle routing problem with preemptive pick-up service (VRPPS) is proposed to systematically describe the problem with potentially complex dynamic priorities among different tasks. Based on a dynamically constructed space-time network, this study formulates a multicommodity flow model that aims at optimizing the generalized service utility and the operating cost simultaneously. To provide a fast value approximation, we present a solution framework deploying the augmented Lagrangian relaxation approach with embedded dynamic programming algorithms. This framework jointly integrates the processes of updating request information and obtaining optimal routes. Finally, the validity and effectiveness of the proposed methods are evaluated on an illustrative network and a real-world last-mile delivery network operated by a logistics company.