Vehicle routing problem with stochastic demand (VRPSD): optimisation by neighbourhood search embedded adaptive ant algorithm (ns-AAA)
by M.R. Nagalakshmi, Mukul Tripathi, Nagesh Shukla, M.K. Tiwari
International Journal of Computer Aided Engineering and Technology (IJCAET), Vol. 1, No. 3, 2009

Abstract: Taking into account the real world applications, this paper considers a vehicle routing problem with stochastic demand (VRPSD) in which the customer demand has been modelled as a stochastic variable. Considering the computational complexity of the problem and to enhance the algorithm performance, a neighbourhood search embedded adaptive ant algorithm (ns-AAA) is proposed as an improvement to the existing ant colony optimisation. The proposed metaheuristic adapts itself to maintain an adequate balance between exploitation and exploration throughout the run of the algorithm. The performance of the proposed methodology is benchmarked against a set of test instances that were generated using design of experiment (DOE) techniques. Besides, analysis of variance (ANOVA) is performed to determine the impact of various factors on the objective function value. The robustness of the proposed algorithm is authenticated against ant colony optimisation and genetic algorithm over which it always demonstrated better results thereby proving its supremacy on the concerned problem.

Online publication date: Sun, 12-Jul-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Aided Engineering and Technology (IJCAET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com