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Volume 299 - The 7th International Conference on Computer Engineering and Networks (CENet2017) - Session I - Machine Learning
Improved Ant Colony Optimization in Express Distribution Routing
T. Liu,* S. Qin, L. Zhang
*corresponding author
Full text: pdf
Pre-published on: 2017 July 17
Published on: 2017 September 06
Abstract
Increasingly complex urban traffic conditions often challenge the express services with long delivery path and much more time as consumed. In this paper, a Traffic Impact Factor (TIF) is introduced to model the impact of urban traffic conditions on express services. Based on the TIF,
an optimization model is constructed to minimize the delivery distance and the time consumed.In the solution , the objective function is defined by incorporating TIF into the probability transfer formula of the Ant Colony Optimization (ACO), which has thereby improved the update rule of pheromone. The improved ACO is suitable for the optimization of the express delivery path. In the experiment, 30 cities express delivery experimental data are used to compare the distribution distance, the delivery time and the objective function value between the ACO and the improved ACO. The result shows that the improved ACO may reduce the distribution cost related to the distribution distance and the time factor
DOI: https://doi.org/10.22323/1.299.0012
Open Access
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