Towards Fast and Accurate Solutions to Vehicle Routing in a Large-Scale and Dynamic Environment

The delivery and courier services are entering a period of rapid change due to the recent technological advancements, E-commerce competition and crowdsourcing business models. These revolutions impose new challenges to the well studied vehicle routing problem by demanding (a) more ad-hoc and near real time computation - as opposed to nightly batch jobs - of delivery routes for large number of delivery locations, and (b) the ability to deal with the dynamism due to the changing traffic conditions on road networks. In this paper, we study the Time-Dependent Vehicle Routing Problem (TDVRP) that enables both efficient and accurate solutions for large number of delivery locations on real world road network. Previous Operation Research (OR) approaches are not suitable to address the aforementioned new challenges in delivery business because they all rely on a time-consuming a priori data-preparation phase (i.e., the computation of a cost matrix between every pair of delivery locations at each time interval). Instead, we propose a spatial-search-based framework that utilizes an on-the-fly shortest path computation eliminating the OR data-preparation phase. To further improve the efficiency, we adaptively choose the more promising delivery locations and operators to reduce unnecessary search of the solution space. Our experiments with real road networks and real traffic data and delivery locations show that our algorithm can solve a TDVRP instance with 1000 delivery locations within 20 min, which is 8 times faster than the state-of-the-art approach, while achieving similar accuracy.