Optimization of Distribution Routes in Agricultural Product Supply Chain Decision Management Based on Improved ALNS Algorithm

The transportation of fresh agricultural products is not conducted along a sufficiently precise route, resulting in an extended transportation time for vehicles and a consequent deterioration in product freshness. Therefore, the study proposes an agricultural product transportation path optimization model based on an optimized adaptive large neighborhood search algorithm. The Solomon standard test case is used for the experiment, and the algorithm before and after optimization is compared. From the results, the optimized method was effective for the distribution model C201, R201, and CR201 sets after conducting case analysis. The total cost of the R201 transportation set was the lowest, while C101 had the highest total cost. The lowest vehicle cost consumption was R201 at 600, and the highest was C101 at 2220. The C101 algorithm took 145 s to calculate, and R201 took 199 s. All values of CR201 were average, with high fault tolerance. The proposed method was used to address the optimal operator solution. The C201 example took 244 s to calculate 2350 objective function values. The R201 example took 239 s to obtain 657 objective function values. The CR201 example took 233 s to obtain 764 objective function values. This indicates that the designed method has a significant effect on optimizing the distribution path of agricultural products. Compared with the unimproved algorithm, it has more accurate search ability and lower transportation costs. This algorithm provides path optimization ideas for the agricultural product transportation industry.

@article{Liu2024,
  title     = {Optimization of Distribution Routes in Agricultural Product Supply Chain Decision Management Based on Improved ALNS Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Liling Liu and Yang Chen and Ao Li},
  doi       = {10.14569/IJACSA.2024.0150828},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150828}
}