Separated vehicle scheduling optimisation for container trucking transportation based on hybrid quantum evolutionary algorithm
by Zheng Wang; Jianfeng Dai
International Journal of Computing Science and Mathematics (IJCSM), Vol. 8, No. 5, 2017

Abstract: To optimise the trucking problem with time windows, a multi-objective mathematical programming model was established for separated vehicle scheduling. To compute Pareto solutions, a phased optimal algorithm based on hybrid quantum evolution was put forward. To enhance the convergence rate, a greedy repair operator was designed. To avoid premature convergence, a neighbourhood search based on node switching was performed. To maintain the dispersion of the Pareto solutions, an adaptive grid operator was designed. The effectiveness of the proposed method compared to previous scheduling modes and other algorithms was verified experimentally. For the same transport capacity, the vehicle scheduling method based on a quantum evolutionary algorithm can greatly reduce both the number of vehicles and cost.

Online publication date: Tue, 14-Nov-2017

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 Computing Science and Mathematics (IJCSM):
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