The optimisation of the location of front distribution centre: A spatio-temporal joint perspective

Front Distribution Centre (FDC) is a new terminal warehouse which is closer to customers, with its location selection being crucial for e-commerce and customer time satisfaction. We introduce in this paper a joint dis-tribution function of demand based on time and space, which constructs two spatio-time models: spatio-time clustering model and spatio-time optimisation model. A staged clustering algorithm is designed to obtain the candidate FDCs, and an intelligent algorithm based on NSGA-II (Non-dominated Sorting Genetic Algorithm II) is applied to determine the final FDCs, in which the location selection problem is formulated as a bi-objective programming model to minimise total costs and maximise customer time satisfaction. Our results indicate that the model considering spatio-temporal joint attribute of demand performs better than the traditional spatial model. Furthermore, when compared with the k-means clustering algorithm, Multi-Objective Evolutionary Al-gorithm based on Decomposition (MOEA/D) and its improved algorithm Multi-Objective Evolutionary Algorithm based on the Adaptive Neighborhood Adjustment strategy (MOEA/D-ANA), Multi-Objective Particle Swarm Optimisation (MOPSO) and its enhancing algorithm Competitive Multi-Objective Particle Swarm Optimiser (CMOPSO), the solving method based on staged clustering and NSGA-II absolutely performs more stable and can get a greater number of pareto-optimal solutions with higher qualities. Especially when compared with K-means clustering algorithms, it can reduce total costs by up to 38.84% and improve customer time satisfaction by up to 36.22%.

Automated summary

This paper introduces a joint distribution function of demand based on time and space, constructing two spatio-time models: spatio-time clustering model and spatio-time optimization model. A staged clustering algorithm is used to obtain candidate FDCs, and an intelligent algorithm based on NSGA-II is applied to determine the final FDCs. The results show that the model considering the spatio-temporal attribute of demand outperforms traditional spatial models.