Multimodal transportation route optimization based on fuzzy demand and fuzzy transportation time
The study of fuzzy multimodal transport path optimization considering uncertainty can enhance the robustness of transport solutions and improve the risk resistance of enterprises while meeting the dual requirements of economic and environmental protection of transport solutions.In this paper,a low-carbon and low-cost multimodal transportation path optimization model with fuzzy demand and fuzzy transportation time is established,and a general coding method based on priority is designed for the problem that continuous metaheuristic algorithm cannot directly solve the discrete combinatorial optimization model.On this basis,a special decoding method with heuristic factors is proposed to further improve the solution quality of the algorithm,and an adaptive differential evolutionary algorithm with neighborhood search strategy is proposed.The results show that the final scheme obtained by the improved algorithm satisfies the constraints in most scenarios of Monte Carlo sampling,and the scheme is stable with the lowest objective value.
uncertainty optimizationfuzzylocal searchdifferential evolutionary algorithmMonte Carlo sampling
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