Efficient Heuristics for Large-Scale Vehicle Routing Problems Using Particle Swarm Optimization

2012 ◽  
Vol 3 (2) ◽  
pp. 34-50
Author(s):  
A. Chandramouli ◽  
L. Vivek Srinivasan ◽  
T. T. Narendran

This paper addresses the Capacitated Vehicle Routing Problem (CVRP) with a homogenous fleet of vehicles serving a large customer base. The authors propose a multi-phase heuristic that clusters the nodes based on proximity, orients them along a route, and allots vehicles. For the final phase of determining the routes for each vehicle, they have developed a Particle Swarm Optimization (PSO) approach. Benchmark datasets as well as hypothetical datasets have been used for computational trials. The proposed heuristic is found to perform exceedingly well even for large problem instances, both in terms of quality of solutions and in terms of computational effort.

2012 ◽  
Vol 253-255 ◽  
pp. 1369-1373
Author(s):  
Tie Jun Wang ◽  
Kai Jun Wu

Multi-depots vehicle routing problem (MDVRP) is a kind of NP combination problem which possesses important practical value. In order to overcome PSO’s premature convergence and slow astringe, a Cloud Adaptive Particle Swarm Optimization(CAPSO) is put forward, it uses the randomicity and stable tendentiousness characteristics of cloud model, adopts different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. In this paper, the algorithm is used to solve MDVRP, a kind of new particles coding method is constructed and the solution algorithm is developed. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability than GA and the PSO algorithm.


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