Abstract
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 distribution 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 Algorithm 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%.
•Based on the introduction of customer demand temporal distribution function, a spatio-temporal clustering model is constructed, utilising a staged clustering algorithm of three-dimensional clustering and then two-dimensional clustering.•A spatio-temporal optimisation model is constructed, simultaneously minimizing total costs and maximising the customer time satisfaction, and then an intelligent algorithm - NSGA-II (Non-dominated Sorting Genetic Algorithm II) is applied.•Compared with K-means clustering algorithm and other multi-objective optimisation methods, the solving method based on staged clustering and NSGA-II shows more stable performance and achieves a greater number of Pareto-optimal solutions with higher qualities, which can provide managers with more alternative solutions to make the results more in line with actual management needs.