Volume 5, Issue 3, June 2019, Page: 37-43
Research on Logistics Distribution Optimization Based on Low Carbon Constraints
Yu Xiaohao, School of Transportation, Shanghai Maritime University, Shanghai, China
Received: Aug. 8, 2019;       Accepted: Sep. 9, 2019;       Published: Sep. 18, 2019
DOI: 10.11648/j.ijtam.20190503.11      View  104      Downloads  23
Abstract
Recently, the problem of climate is becoming more and more serious and the low carbon idea is accepted by people gradually. Meanwhile, the China’s logistic develop rapidly. Whereas, distribution as the key part, its importance is obvious to see. So, it is significant to research the distribution of logistic activity based on low-carbon with the low-carbon economy put forward. In this dissertation, the paper analyzes and summarizes the research at home and abroad. And then it distinguishes the different distribution model from economy, service and carbon. Besides, it also combines the joint distribution, the green logistic, circular logistic and reverse logistic to contrast. Based on this, the paper propose the way of calculating carbon emission and then build the math model of calculating carbon emission during distribution activity. At last, it uses the genetic algorithm as a tool to set up a experience platform. By using the platform, it researches the distribution math model based on the carbon emission. It also analyzes the result of the experience, and improve the data of the genetic algorithm.
Keywords
Logistics Distribution, Carbon Emissions, Genetic Algorithm
To cite this article
Yu Xiaohao, Research on Logistics Distribution Optimization Based on Low Carbon Constraints, International Journal of Theoretical and Applied Mathematics. Vol. 5, No. 3, 2019, pp. 37-43. doi: 10.11648/j.ijtam.20190503.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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