Abstract This paper investigates the multi-route planning problem of multimodal transportation for oversize and heavyweight cargo based on reconstruction (MM-OHC-R). Considering required reconstruction of lines or nodes, a reconstruction model for route planning of OHC is proposed. The model aims to simultaneously determine the transportation route, modes of transport as well as the lines or nodes required to be reconstructed to minimize the total cost. To attain multiple solutions, the K shortest paths algorithm is introduced and improved to solve MM-OHC-R problem. The proposed algorithm is derived from a virtual network named multi-prism network and Yen algorithm. Moreover, the algorithm is improved by introducing improved A* algorithm. A case study is conducted to validate the feasibility of the proposed model and algorithms. The results demonstrate that the usage of reconstruction measurements is able to optimize the transportation schemes and the proposed algorithm is capable of developing multiple transportation schemes to provide the support of decision making and risk prevention and control for the carrier. For the constrained MM-OHC-R problem, the improvement of the A* algorithm enhances the computation performance by reducing the size of the candidate path set effectively.

中文翻译：

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基于重构的超大重量货物多式联运多路线规划

摘要 本文研究了基于重构(MM-OHC-R)的超大重量货物多式联运多路线规划问题。考虑到线路或节点需要重构，提出了OHC路径规划的重构模型。该模型旨在同时确定运输路线、运输方式以及需要重建的线路或节点，以最小化总成本。为了获得多种解，引入并改进了K最短路径算法来解决MM-OHC-R问题。所提出的算法来源于一个名为多棱镜网络的虚拟网络和Yen算法。此外，通过引入改进的A*算法对算法进行了改进。进行案例研究以验证所提出的模型和算法的可行性。结果表明，使用重构测量能够优化运输方案，并且所提出的算法能够开发多种运输方案，为承运人的决策和风险防控提供支持。对于有约束的MM-OHC-R问题，A*算法的改进通过有效减少候选路径集的大小来提高计算性能。