Fuzzy robust optimization of low-carbon multimodal transport paths under mixed uncertainty
XU Guoquan
ZHENG Rui
YUAN Shaoqiu
XIONG Dianzhen
Abstract：In addressing the impact of various uncertain factors,including transport time and freight volume,on multimodal transport path selection,a fuzzy robust regret model is proposed.This model integrates fuzzy opportunity constraint programming and robust optimization to comprehensively manage uncertain variables.First,triangular fuzzy numbers are used to represent the uncertainty in transport time parameters,while the robust optimization scenario method is applied to handle fluctuations in freight volume.Hybrid time window constraints are introduced,and fuzzy time parameters are clarified using fuzzy opportunity constraint program-ming.The model optimizes total cost,total carbon emissions,and total time as its objectives.Subsequently,to improve the convergence quality of the algorithm and maintain population diversity,an improved adaptive Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)is designed with local optimization strategies.The algorithm's performance is compared across four practical multimodal transport scenarios with different node sizes.Finally,a complex virtual example is used to validate the model and analyze the robustness of the solution in response to uncertainty.The results demonstrate that,in comparison with the conventional NSGA-Ⅱ,the improved daptive NSGA-Ⅱ exhibits superior performance in the two objectives of total cost and total carbon emission as node size increases.When the node network reaches 30,the adaptability of the two objectives experiences a decline by 25.22%and 26.39%,respectively.The robust solution obtained from the model can effectively adapt to an uncertain transportation environment and changes in the decision maker's preferences.Multimodal transportation decision makers need to comprehensively consider the im-pact of uncertain factors,select appropriate regret values and confidence levels for fuzzy parameters,and achieve transportation solutions that align with their preferences.
Keywords：transportation planning and managementlow-carbon multimodal transportrobust optimi-zationmixed uncertaintyimproved adaptive NSGA-Ⅱ
Publication Date：2025-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：16( 55-70 )