Study on application of improved chaos ant colony algorithm in vehicle routing problem with time windows

Author(s):  
Hai Yang
Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.


2014 ◽  
Vol 1030-1032 ◽  
pp. 1941-1944
Author(s):  
Hong Dou Zhang ◽  
Ning Guo ◽  
Jian Lin Mao ◽  
Hai Feng Wang

Vehicle routing problem with time Windows (VRPTW) that is a kind of important extension type for VPR. In view of problem which the ant colony algorithm in solving VRPTW easily plunged into local optimum , this paper defines a new ant transition probability of saving ideas, and uses the Pareto optimal solution set of global pheromone updating rule, and puts forward a kind of improved Pareto ant colony algorithm (IPACA) . Through the simulation experiments show that IPACA improves the global search ability of ACA, effectively avoids the algorithm falls into local optimum, and reduces the total distribution cost (distance), so as to verify the effectiveness of the proposed algorithm.


2013 ◽  
Vol 706-708 ◽  
pp. 855-858
Author(s):  
Jian Wang ◽  
Hong Yun Li ◽  
Hong Chen

To optimize the vehicle routing problem with time windows(VRPTW), a mixed ant colony algorithm (MACO) was proposed to accomplish the vehicles’ scheduling. The pheromone adaptive volatile strategy takes real-time traffic status into consideration. Algorithm was accomplished on computer with the c# language.10 examples were calculated. Results show, MACO has a quick convergence rate, the result is stable.


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