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

Author

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  • Chen, Linlin
  • Han, Shuihua
  • Ye, Zhen
  • Xia, Senmao

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%.

Suggested Citation

  • Chen, Linlin & Han, Shuihua & Ye, Zhen & Xia, Senmao, 2023. "The optimisation of the location of front distribution centre: A spatio-temporal joint perspective," International Journal of Production Economics, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:proeco:v:263:y:2023:i:c:s0925527323001822
    DOI: 10.1016/j.ijpe.2023.108950
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