Online Parallel optimization Approach to Courier Routing Problems*

Abstract

Contemporary electronic marketing leads to massive requirements of courier services in China. A local outlet providing delivery and real-time pickup services, however, severely depends on the good staff of experience to handle the routing tasks. These routing tasks are formulated as a one-to-many-to-one dynamic pickup and delivery problem.In this research, we have developed an online method to solve routing problems. With adaptive memory and heuristic insertion for a speedy response, this method generates results by taking both quality and responsiveness into account, based on simulated annealing to optimize untraveled routes during the trip. This real-time method enables to establish a real-time route planning system: after initialization, adaptive memory is built up to contain the multiple candidate solutions and updated by real-time optimization responding to real-time requests insertion; once a dispatching order is needed, the best solution from the adaptive memory will be selected.By testing on simulated data with different dynamism level, we have gained good results of both better responsiveness and quality than that of the greedy algorithm, and showing that data with high dynamism can also have low-cost solutions. This work contributes to reducing human involvement in real-time courier service.