Two-echelon logistics distribution region partitioning problem based on a hybrid particle swarm optimization–genetic algorithm

• A logistics distribution region partitioning model is developed.
• This model is to minimize the cost of two-echelon logistics distribution network.
• A hybrid algorithm with PSO and GA is proposed.
• The empirical results reveal that EPSO–GA algorithm outperforms other algorithms.

Two-echelon logistics distribution region partitioning is a critical step to optimize two or multi-echelon logistics distribution network, and it aims to assign distribution unit to a certain logistics facility (i.e. logistic center and distribution center). Given the partitioned regions, vehicle routing problem can be further developed and solved. This paper established a model to minimize the total cost of the two-echelon logistics distribution network. A hybrid algorithm named as the Extended Particle Swarm Optimization and Genetic Algorithm (EPSO–GA) is proposed to tackle the model formulation. A two-dimensional particle encoding method is adopted to generate the initial population of particles. EPSO–GA combines the merits of Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) with both global and local search capability. By updating the inertia weight and exchanging best-fit solutions and worst-fit solutions between PSO and GA, EPSO–GA algorithm is able to converge to an optimal solution with a reasonable design of termination and iteration rules. The computation results from a case study in Guiyang city, China, reveal that EPSO–GA algorithm is superior to the other three algorithms, Hybrid Particle Swarm Optimization (HPSO), GA, and Ant Colony Optimization (ACO), in terms of the partitioning schemes, the total cost and number of iterations. By comparing with the exact method, the proposed approach demonstrates its capability to optimize a small scale two-echelon logistics distribution network. The proposed approach can be readily implemented in practice to assist the logistics operators reduce operational costs and improve customer service. In addition, the proposed approach is of great potential to apply in other research domains.