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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 893851, 13 pages
http://dx.doi.org/10.1155/2014/893851
Research Article

The Research on Low Carbon Logistics Routing Optimization Based on DNA-Ant Colony Algorithm

1Information Engineering College, Tianjin University of Commerce, Tianjin 300134, China
2Economic College, Tianjin University of Commerce, Tianjin 300134, China
3Waston Engineering Shool, State University of New York at Binghamton, 138 Conklin Avenue, Binghamton, NY 13902, USA

Received 11 April 2014; Accepted 15 May 2014; Published 22 June 2014

Academic Editor: Xiang Li

Copyright © 2014 Liyi Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As the energy conservation and emission reduction and sustainable development have become the hot topics in the world, low carbon issues catch more and more attention. Logistics, which is one of the important economic activities, plays a crucial role in the low carbon development. Logistics leads to some significant issues about consuming energy and carbon emissions. Therefore, reducing energy consumption and carbon emissions has become the inevitable trend for logistics industry. Low carbon logistics is introduced in these situations. In this paper, from the microcosmic aspects, we will bring the low carbon idea in the path optimization issues and change the amount of carbon emissions into carbon emissions cost to establish the path optimization model based on the optimization objectives of the lowest cost of carbon emissions. According to different levels of air pollution, we will establish the double objectives path optimization model with the consideration of carbon emissions cost and economy cost. Use DNA-ant colony algorithm to optimize and simulate the model. The simulation indicates that DNA-ant colony algorithm could find a more reasonable solution for low carbon logistics path optimization problems.