The application of the improved hybrid Ant Colony Algorithm in Vehicle Routing optimization problem

Author(s):  
Rongwen Zhang ◽  
Shaomei Zhou
2013 ◽  
Vol 385-386 ◽  
pp. 1917-1920
Author(s):  
Rui Wang ◽  
Zai Tang Wang

This paper analyzes the domestic and international logistics distribution route optimization problem and the research status of ant colony algorithm, illustrates the problems existing in the logistics distribution now. It reflects the necessity to research on the vehicle routing optimization problem. In order to increasing the ant colony algorithm’s convergence speed and avoiding to fall into local optimum, we improve the pheromone evaporation coefficient and visibility to optimize the searching ability, which can avoid premature convergence and stagnation.


2014 ◽  
Vol 556-562 ◽  
pp. 4693-4696
Author(s):  
Yue Li Li ◽  
Ai Hua Ren

With the development of the market economy, the logistics industry has been developed rapidly.It is easy to understand that good vehicle travel path planning has very important significance in the logistics company,especially in the general production enterprises. This paper mainly studies the microcosmic traffic system in the type of vehicle routing problems: capacity-constrained vehicle routing problem. We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem, treated as nodes in a spatial network. For the networks where the nodes are concentrated, the use of hybrid heuristic optimization can greatly improve the efficiency of the solution. The algorithm produces high-quality solutions for the capacity-constrained vehicle routing problem.


2013 ◽  
Vol 711 ◽  
pp. 816-821
Author(s):  
Zhi Ping Hou ◽  
Feng Jin ◽  
Qin Jian Yuan ◽  
Yong Yi Li

Vehicle Routing Problem (VRP) is a typical combinatorial optimization problem. A new type of bionic algorithm-ant colony algorithm is very appropriate to solve Vehicle Routing Problem because of its positive feedback, robustness, parallel computing and collaboration features. In view of the taxi route optimization problem, this article raised the issue of the control of the taxi, by using the Geographic Information System (GIS), through the establishment of the SMS platform and reasonable taxi dispatch control center, combining ant colony algorithm to find the most nearest no-load taxi from the passenger, and giving the no-load taxi the best path to the passenger. Finally this paper use Ant Colony laboratory to give the simulation. By using this way of control, taxis can avoid the no-load problem effectively, so that the human and material resources can also achieve savings.


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