Abstract
The research on the optimization of a low-carbon multimodal transportation path under uncertainty can have an important theoretical and practical significance in the high-quality development situation. This paper investigates the low-carbon path optimization problem under dual uncertainty. A hybrid robust stochastic optimization (HRSO) model is established considering the transportation cost, time cost and carbon emission cost. In order to solve this problem, a catastrophic adaptive genetic algorithm (CA-GA) based on Monte Carlo sampling is designed and tested for validity. The multimodal transportation schemes and costs under different modes are compared, and the impacts of uncertain parameters are analyzed by a 15-node multimodal transportation network numerical example. The results show that: (1) the uncertain mode will affect the decision-making of multimodal transportation, including the route and mode; (2) robust optimization with uncertain demand will increase the total cost of low-carbon multimodal transportation due to the pursuit of stability; (3) the influence of time uncertainty on the total cost is significant and fuzzy, showing the trend of an irregular wave-shaped change, like the ups and downs of the mountains. The model and algorithm we proposed can provide a theoretical basis for the administrative department and logistic services providers to optimize the transportation scheme under uncertainty.
Highlights
With the rapid growth of the world economy and production scale, fossil fuel consumption and carbon dioxide emission from combustion increase day by day, and the phenomena of climate warming and environmental pollution are constantly highlighted.The resulting problems, such as “global warming” and “ecosystem deterioration”, are seriously threatening the living environment and development space of human beings [1].In recent years, the government of China has actively taken measures to reduce carbon emissions and advocate the development concept of “green GDP”
Under the new economic situation of high-quality development, the low-carbon multimodal transportation route optimization research has important theoretical and practical significance, which can simultaneously meet the practical needs of the market, the economy and environmental protection, so as to realize energy conservation and emission reduction, promote the adjustment and upgrading of industrial structure and advance the low-carbon management of transportation industry
Proposed a random multi-objective model of forward/reverse logistics network designing under an uncertain environment [29]; Demirel et al, (2014) solved the problem of multimodal transportation route selection in the case of fuzzy demand by introducing triangular fuzzy numbers [30]
Summary
With the rapid growth of the world economy and production scale, fossil fuel consumption and carbon dioxide emission from combustion increase day by day, and the phenomena of climate warming and environmental pollution are constantly highlighted. The data on cargo transportation in China in the past five years show that the proportion of road transportation is about 75% and presented an increasing trend year by year, while the proportion of railway and waterway transportation is about 10% and 14%, respectively This proportion feature has failed to make full use of the advantages of the railway and waterway in transportation costs and carbon emission. Under the new economic situation of high-quality development, the low-carbon multimodal transportation route optimization research has important theoretical and practical significance, which can simultaneously meet the practical needs of the market, the economy and environmental protection, so as to realize energy conservation and emission reduction, promote the adjustment and upgrading of industrial structure and advance the low-carbon management of transportation industry.
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