Optimization of Cold Chain Multimodal Transportation Route Under Carbon Trading Price Uncertainty
This paper addresses the problem of optimizing cold chain multimodal transportation routes under uncertainty of carbon trading prices.Triangular fuzzy numbers are employed to describe the carbon trading price,taking into account the characteristics of price fluctuations in the current market.The risk preference level of multimodal transport operators and the customer's soft and hard transport time window requirements are combined to establish the fuzzy credibility function for the carbon trading price and the customer satisfaction function,respectively.Accordingly,a bi-objective optimization model considering transportation cost and customer satisfaction is constructed,and a fuzzy adaptive non-dominated sorting genetic algorithm(FANSGA-Ⅱ)is designed.The numerical example results show that compared with the random uniform distribution method,the triangular fuzzy number method can enhance the stability of the optimization results and reduce the coefficient of variation of transportation cost from 20.3%to approximately 4.4%.The results of the sensitivity analysis indicate that the level of risk preference of multimodal transport operators is positively correlated with the proportion of railroad and waterway transport modes.Furthermore,the implementation of a carbon trading policy is predicted to result in a reduction in carbon emissions by at least 7%.The lower the refrigeration temperature of the cargo,the more sensitive the carbon trading risk preference value is to the choice of transportation mode.However,once the risk preference value reaches a specific value,the chosen transportation mode will no longer change.The findings of the study can provide a reference for multimodal transport operators to develop cold chain transportation options.
万方数据
维普
