Optimization models of integrated load consolidation and transportation mode selection problem on intermodal network

This thesis deals with clustering, three dimensional bin packing, pick-up routing, train assignment and transport mode assignment problems for intermodal transportation networks. The research problem is called pick-up routing problem with a three dimensional (3D-PRP) loading constraints, clustered backhauls at operational level, and train loading and transport mode selection at tactical level for an intermodal transportation network. During the first stage, we propose a hybrid approach, which packs the orders firstly using machine learning, then routing using a genetic algorithm approach. During the second stage, a mathematical model is formulated to solve the train-loading problem to minimize the total costs by incorporating various cost types, in which detention and demurrage costs are taken into account. Allocation of export containers to transportation modes is also provided. A fuzzy-based approach is performed to determine the risk weights of the transportation modes. Developed graphical user interface allows the end-user to control the calculations visually. Computational results for real-world data and new randomly generated instances reveal that the proposed approaches produce highquality solutions.