Abstract
At present, it is difficult to complete scheduling efficiently and cost-effectively in the process of logistics and distribution in the complex background, such as multi-target transportation, multi-model and multi-storage. Based on the detailed analysis of this problem, a multi-objective mixed integer linear distribution cost optimization model is established under multi-resource constraints. The model adopts the method of expanding the scale of calculation to enrich the vehicle distribution scheme. A linear representation of nonlinear cost is proposed with the mutual restraint way of four constraint modules which includes actual capacity, dynamic balance, daily running time and cost constraint. It can resolve the practical problems including customer loading queue, time consumption of path selection, and daily idleness of vehicles. Through the simulation experiments, the results show that the model can not only obtain the optimal distribution scheme with the fewest cost, but also have strong stability, wide practical application and scalability.
This work is supported by the Technology Planning Project of Guangdong Province (No.: 2017A040406023) and the Technology Planning Project of Guangzhou City (No.: 201804010353).
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Wang, J. et al. (2020). Research on Optimization of Multi-target Logistics Distribution Based on Hybrid Integer Linear Programming Model. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_15
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DOI: https://doi.org/10.1007/978-981-15-5577-0_15
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