Optimizing express courier vehicle routing for dynamic demands of IoT customers

To meet the dynamic demands of Internet of Things (IoT) customers for express pickup and transportation, and to address the time-varying customer requirements and the modeling and solving methods for vehicle routing optimization based on cost and time windows, this study conducts research on the optimization of express pickup vehicle routing under the constraints of vehicle capacity, time, and dynamic customer demands. Considering the impact of vehicle loading capacity constraints, time window constraints, penalties, and dynamic demands on existing solutions, this paper establishes a mathematical model for route optimization of express pickup with the goal of minimizing transportation costs and penalty costs. In response to the actual situation of dynamic demands of IoT customers, the tabu search method is improved by introducing time slicing and following the principle of minimizing the disruption to customers who have already been picked up, using an improved tabu search algorithm. Finally, using the customer instance data from Solomon case library R104, the optimization of express pickup transportation routes under dynamic customer demands is carried out. The optimization algorithm proposed in this paper is compared with traditional heuristic algorithms to verify the effectiveness of the model and algorithm proposed in this paper. The results show that the improved tabu search algorithm adopted in this study achieves a 2.67% increase in vehicle full load rate and a 0.03% decrease in time utilization rate compared with conventional static route optimization problems when solving the problem of responding to dynamic demands of IoT customers, which is satisfactory. The research results can provide a reference for the selection of express pickup vehicle routes under dynamic demands of IoT customers, achieving the goal of improving express pickup efficiency and reducing costs.