Service network design problems arise at airlines, trucking companies, and railroads wherever there is a need to determine cost-minimizing routes and schedules, given resource availability and service constraints. In recent years, the application of consolidation-based service network design in the express service has attracted lots of academic attention due to the rapid growth of the express industry. This paper studies the consolidation-based service network design problem, which jointly determines the commodity flow, vehicle dispatching, and fleet sizing. We propose a mixed-integer optimization model to address the problem and design an efficient iterative backbone algorithm to solve large-scale real-world problems. The numerical results of large-scale instances confirmed that the solution obtained by our proposed algorithm is better than that of the primal model, and the running time taken is less than half that of the general solution approach. The computational study confirmed the effectiveness and efficiency of the proposed algorithm.

Service-network design in transportation applications has attracted much scientific attention due to the rapid growth of online shopping. Practical service-network planning tools could help express service providers in minimizing the total cost while improving service levels. Efficient service network design is a requirement for sustainable logistical development. Express delivery has substantial negative environmental impacts, and service network design minimizes the environmental impact by reducing energy consumption costs. This paper addresses the service network design problem, which integrates a heterogeneous fleet of vehicles for vehicle dispatching in a consolidation-based time–space network to reflect the express service scenarios. Due to the NP-hard nature of this problem, we designed a layer-based relaxation algorithm to solve large-scale applications. The relaxation method relaxes and fixes the network structure on a layer-by-layer basis, and the computational experience confirms the effectiveness and efficiency of the relaxation algorithm. The solution time and quality are both improved significantly.