Optimization of Storage Paths for Finished Cigarette Logistics Distribution Based on Improved GA-A*

Abstract

To reduce the problems in the transportation process of finished cigarette products, we proposed an improved GA-A* based storage path optimization method for the logistics distribution and storage. A costing model is first introduced for the loss of finished cigarette products in the transportation process, and the A* algorithm (A-star, A*) is used to solve for distribution within different regions, and finally it is combined with the Genetic Algorithm (GA) to establish an optimal path planning model based on minimum cost. The results show that in the convergence comparison between Solomn and Gehring datasets, the research method was able to obtain the minimum objective function value at the 41st and 32nd iterations, and reached a steady state. The error results for the path planning showed that the research method was able to achieve the set target value and meet the performance requirements by the 27th iteration. In practice, the method starts to stabilize and reach the optimal state at around 47 iterations under the same conditions. In all, the method is faster than other methods in terms of convergence, smaller planning errors and higher accuracy, and is more feasible and effective in the distribution route planning of finished cigarette logistics.

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