Platooning is the coupling of two or more trucks with small inter-vehicle distance. This technology has the potential to improve road safety, to increase road capacity, and to reduce CO2 emissions. This thesis focuses on minimizing the overall fuel consumption by the centralized formation of vehicles into platoons. Several novel solution strategies are developed for efficiently and effectively tackling large-scale problem instances. One of these strategies stipulates the grouping of vehicles with high platooning incentives and subsequent computation of platooning routes for each vehicle group independently of the other vehicles. This work proposes three lightweight incentive methods based on inexpensive geometric operations. Several grouping approaches, which are using these incentives, are developed to coordinate the vehicles and compared against each other for their efficiency and effectiveness. Our experimental evaluation demonstrates that our proposed grouping-based routing algorithms are more scalable than exact solvers and state-of-the-art routing methods for vehicle platooning. Furthermore, this thesis introduces a new efficient Column Generation formulation for the Vehicle Platooning Problem. An equivalent path-based Master Problem (MP) is given for the Vehicle Platooning Problem (VPP), where the number of variables is much larger than the number of constraints. The Column Generation method restricts and solves the MP iteratively. The corresponding subproblems generate new promising platooning paths and add them to the Restricted Master Problem (RMP). The RMP can be solved very efficiently as only a sufficiently small subset of variables is considered at a time. The composition of our grouping algorithms and our Column Generation (CG)-based routing algorithm enables us to solve extensive problem instances with up to 2000 vehicles on road networks with up to 300,000 edges.


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    Title :

    New heuristics for finding optimal routes for vehicle platoons in road networks


    Contributors:

    Publication date :

    2020



    Type of media :

    Miscellaneous


    Type of material :

    Electronic Resource


    Language :

    English





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