Study on Vehicle Routing and Scheduling Problems in Underground Mine Based on Adaptively ACA

Qing Hua Gu; Shi Gun Jing

doi:10.4028/www.scientific.net/AMM.157-158.1293

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 157-158Study on Vehicle Routing and Scheduling Problems...

Study on Vehicle Routing and Scheduling Problems in Underground Mine Based on Adaptively ACA

Abstract:

For vehicle routing optimization problem in the underground mine, a famous NP- Hard problem is put forward. This paper uses improved ant colony algorithm (ACA) to solve the problem. Basic ant colony algorithm (ACA) has many shortages, such as long searching time， slow convergence rate and easily limited to local optimal solution etc. The improved ant colony algorithm is proposed to overcome these shortcomings and improve its performance adaptively. In every iteration of the ant colony algorithm, adaptive evaporating coefficient is selected to control the convergence rate at first. And the power of this approach was demonstrated on a test case. The results derived from basic ACA and the improved ACA are compared and analyzed in the experiment. It proved that the improved ant colony algorithm is effective

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Periodical:

Applied Mechanics and Materials (Volumes 157-158)

Pages:

1293-1296

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.157-158.1293

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Online since:

February 2012

Authors:

Qing Hua Gu, Shi Gun Jing

Keywords:

Improved ACA, Pheromone, Tunnel, Vehicle Routing Problem (VRP)

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References

[1] H. B. Duan, Ant colony algorithm and its application(Science Press, Beijing, 2005).

Google Scholar

[2] M. Dorigo, L. M Gambardella. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem[J]. IEEE Transactions on Evolutionary Coputation, 01, (1997),P. 53-66.

DOI: 10.1109/4235.585892

Google Scholar

[3] X.L. CUI, L. MA, B.Q. FAN, Ant searching algorithm for vehicle routing problem, Journal of Systems Engineering, 04, (2004),P. 418-422.

Google Scholar

[4] J.L. LI, C. W LU, J. CH LI. Study on vehicle scheduling problem with time windows based on ant colony algorithm. Information Technology, 05, (2006),P. 128-131.

Google Scholar

[5] Zh. X ZHANG; B. L SHAO, Vehicle Routing and Scheduling Problems Based on Improved ACA , Journal of Highway and Transportation Research and Development, 04, (2008),P. 137-140.

Google Scholar

[6] G. L QIN, J. B YANG, AN IMPROVED ANT COLONY ALGORITHM BASED ON ADJUSTING PHEROMONE, Information and Control, vol. 31(3), (2002),P. 198-210.

Google Scholar