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Mathematical Problems in Engineering
Volume 2018, Article ID 8961505, 9 pages
https://doi.org/10.1155/2018/8961505
Research Article

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

1College of Business Administration, Hunan University, No. 11 Lushan South Road, Changsha 410082, China
2Business School, Hunan University of Science and Technology, Taoyuan Road, Xiangtan 411201, China
3Liverpool Business School, Liverpool John Moores University, Redmonds Building, Brownlow Hill, Liverpool L3 5UX, UK

Correspondence should be addressed to Mi-Yuan Shan; moc.361@jgnauyimnahs

Received 16 November 2017; Accepted 28 February 2018; Published 11 April 2018

Academic Editor: Salvatore Strano

Copyright © 2018 Xiao-Hong Liu 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

Green Vehicle Routing Optimization Problem (GVROP) is currently a scientific research problem that takes into account the environmental impact and resource efficiency. Therefore, the optimal allocation of resources and the carbon emission in GVROP are becoming more and more important. In order to improve the delivery efficiency and reduce the cost of distribution requirements through intelligent optimization method, a novel multiobjective hybrid quantum immune algorithm based on cloud model (C-HQIA) is put forward. Simultaneously, the computational results have proved that the C-HQIA is an efficient algorithm for the GVROP. We also found that the parameter optimization of the C-HQIA is related to the types of artificial intelligence algorithms. Consequently, the GVROP and the C-HQIA have important theoretical and practical significance.