MATEC Web Conf.
Volume 325, 20202020 8th International Conference on Traffic and Logistic Engineering (ICTLE 2020)
|Number of page(s)||5|
|Section||City Logistics and Distribution Services|
|Published online||22 October 2020|
Multi-objective Optimization Model of Multi-modal Transport Based on Regional Sustainability Indicators
School of Transportation, Shandong University of Science and Technology, 266590 Qingdao, China
With the development of “One Belt, One Road” initiative and free trade area, the volume of cross-border international logistics involving multiple modes of transport has surged. Meanwhile, the proportion of using integrated transportation system in domestic trunk transport has increased. Multi-modal transport (MMT) based on green transport can realize intensive utilization of transport capacity resources, and implement sustainable transport management with three bottom lines of economic, environmental and social aspects. In this paper, the carbon emission index and regional transportation infrastructure utilization index are introduced to construct a multi-objective optimization model with sustainable goals of environmental protection, cost saving and social contribution. The poly-population genetic algorithm (PPGA) is used to overcome the limitation of the traditional genetic algorithm running to the local optimum. The model proposed by this paper quantifies environmental and social indicators, balances comprehensive performance of environment, economy and society, and provides quantitative decision making support for carriers, international freight forwarder or third party logistics to carry out green MMT.
Key words: Sustainable transportation / Multi-modal transport / Multi-objective optimization / Poly-population genetic algorithm
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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