Multi-objective Routing Optimization Model and Algorithm for Multimodal Transportation with Uncertain Time
Multimodal transportation leverages the advantages of various transport modes,contributing to cost reduction and efficiency improvements in freight logistics,with route decision-making being a critical factor.The organization of multimodal operations and external environmental changes can cause fluctuations in transportation times.This study considers the impact of stochastic transportation times and transfer times on route optimization in multimodal transportation by introducing trapezoidal fuzzy numbers to represent time uncertainty.A time-window-constrained multimodal transportation route optimization model is constructed with the objectives of minimizing transportation costs,carbon emissions,and transportation time.Based on fuzzy chance-constrained programming theory,the uncertainty model is transformed into a more tractable mixed-integer programming model.The evolutionary process is divided into two stages based on the real-time state of the population:the first stage focuses on optimizing the objective function,while the second stage objective optimization with constraint satisfaction.On this basis,a multi-stage multi-objective evolutionary algorithm is designed to solve the model.Finally,a case study of a multimodal transportation network demonstrates that the proposed method effectively generates a set of route optimization solutions under uncertain transportation times,with chance constraint satisfaction probabilities exceeding 90%.Compared to the state-of-the-art constrained multi-objective evolutionary algorithms,the hypervolume indicator improves by 2.11%to 41.95%,showing significant performance gains and providing effective route decision-making support for multimodal transportation operators.
万方数据
维普
