Study on Multi-Depot Vehicle Routing Problem Based on Cloud Adaptive Particle Swarm Optimization

Tie Jun Wang; Kai Jun Wu

doi:10.4028/www.scientific.net/AMM.253-255.1369

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Lane-Based Road Network Model and its Application in Simulation Road Network Modeling
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Optimization of Vehicle Routing Problem Based on Multi-Objective Genetic Algorithm
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Research of Vehicle Scheduling Problem and Algorithm Based on Emergency Needs
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Research on the Traffic Simulation Platform Based on the Real-Time Mobile Phone Data
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Study on Multi-Depot Vehicle Routing Problem Based on Cloud Adaptive Particle Swarm Optimization
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Model Reference Adaptive Algorithm Designed for Automatic Train Braking Control
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Evaluating Flight Delays Rating Using Fuzzy Simulation Approach
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Algorithm Design of Micro Inertial Strap-Down Heading and Attitude System
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Studying the Effect of Coordination on the Drivers Behavior during Signal Change Intervals at a Typical Intersection in Tokyo, Japan ~ Detailed Methodology and Hypotheses
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 253-255Study on Multi-Depot Vehicle Routing Problem Based...

Study on Multi-Depot Vehicle Routing Problem Based on Cloud Adaptive Particle Swarm Optimization

Abstract:

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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Periodical:

Applied Mechanics and Materials (Volumes 253-255)

Pages:

1369-1373

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.253-255.1369

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Online since:

December 2012

Authors:

Tie Jun Wang, Kai Jun Wu

Keywords:

Cloud Theory, Multi-Depot, Particle Swarm Optimization (PSO), Vehicle Routing Problem

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References

[1] G.Dantzig, J. Ramser.The truck dispatching problem [J].Managment Science, 1959(6):58-102.

Google Scholar

[2] X.B. WANG,Y.J. LI. Study on multi-depot and multi-type vehicles vehicle routing problem with backhauls [J]. Control and Decision, 2009, 24(12):1769-1774. (In Chinese)

Google Scholar

[3] M.J. CHEN, Z.S. ZHANG and C.Y.CHEN. Study on A Novel Clustering Ant Colony Algorithms for MDVRP [J]. Manufacture Information Engineering of China, 2008, 37(11):1-5. (In Chinese)

Google Scholar

[4] J. Kennedy, R. Eberhart. Particle Swarm Optimization [A]. in: Proceedings of IEEE International Conference on Neural Networks[C]. 1995. 1942-1948.

Google Scholar

[5] Z.X. LIU. Vehicle scheduling optimization in logistics distribution based on particle swarm optimization algorithm [J]. Journal of Wuhan University of Science and Technology, 2009, 32 (6):615-618. (In Chinese)

Google Scholar

[6] Y.B. ZHANG, G.Q. LV. Study of Physical Distribution Routing Optimization Problem Based on Hybrid PSO Algorithm [J]. Packing engineering, 2007, 28 (5): 10-12. (In Chinese)

Google Scholar

[7] D.Y. LI, H.J. MENG and X.M. SHI. Membership clouds and membership cloud generators [J]. Computer R&D, 1995, 32(6):15-20. (In Chinese)

Google Scholar