| In recent years,the multimodal transport has drawn more and more attention with the relevant national plans and policies proposed.The advanced stage of transport development is multimodal transportation.Routing optimization is one of the important research directions of multimodal transportation,which is also the extension and expansion of the classic shortest path problem.Combining the real transportation environment with an innovative approach to improve transportation efficiency,reduce transportation cost and satisfy customer requirements is the core of the route optimization problem.This paper considers the timetable limitation and uncertain transport environment in multimodal transport,and studies the importance of timetable in multimodal transport route optimization and model improvement.In this regard,a biobjective multimodal transportation route optimization model with minimum transportation time and cost under uncertain environment is constructed.In terms of algorithmic solution,this paper conducts a lot of valuable research by designing and improving the heuristic algorithm.The main contents of this paper are as follows:First,this paper explains the research background and significance,reviews domestic and foreign research literature on multimodal transport and illustrates the connotation of multimodal transport,multi-objective optimization theory,uncertainty theory and so on.Moreover,it describes how to express the timetable limitation in the constructed model detailly,and designs and improves the operators of each part of the evolutionary algorithm to solve the problem proposed in this paper.Second,the route optimization of multimodal transport network is affected by the uncertain transport duration between nodes.The interval number is used to deal with the uncertainty of transportation duration,and the multi-objective robust optimization model is established which covers the transportation duration and the cost.To solve the combinatorial optimization problem of this study,the Non-Dominated Sorting Genetic Algorithm-II(NSGA-II)is designed,which integrates the(?(10)?)selection method elite retention and the external filing elite retention.Our findings verify the efficiency of the proposed approach by analyzing the diversity,distribution and convergence of the frontier solutions.Finally,near-optimal solutions are obtained with the proposed algorithm in the numerical example.The present study can provide decision reference for multimodal transport carriers in making transport plan.Finally,we study the uncertainty of transportation time and cost,the timetable limitation of selected modes,and the storage cost incurred in advance or delay arriving of the goods in the route optimization of multimodal transport.Considering the above factors comprehensively,this paper establishes a multi-modal bi-objective route optimization model which aims to minimize total transportation time and cost.In terms of methodological contribution,Monte Carlo(MC)simulation approach is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed.Moreover,we propose an efficient transformation method based on data drive to overcome the high time-consuming problem brought by MC simulation.Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGAII algorithm.Finally,numerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the path decision.This study could be used as a reference for decision-maker to make transportation plans. |
PDF Full Text Request