The vehicle platooning problem: Computational complexity and heuristics

•Develops a framework for modeling platooning vehicles traveling in road networks.•Defines the vehicle platooning problem and proves finding its optimum is NP-hard.•Presents heuristics that can solve large instances of the platooning problem. We create a mathematical framework for modeling trucks tr...

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Vydané v:Transportation research. Part C, Emerging technologies Ročník 60; číslo C; s. 258 - 277
Hlavní autori: Larsson, Erik, Sennton, Gustav, Larson, Jeffrey
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier India Pvt Ltd 01.11.2015
Elsevier
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ISSN:0968-090X, 1879-2359, 1879-2359
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Shrnutí:•Develops a framework for modeling platooning vehicles traveling in road networks.•Defines the vehicle platooning problem and proves finding its optimum is NP-hard.•Presents heuristics that can solve large instances of the platooning problem. We create a mathematical framework for modeling trucks traveling in road networks, and we define a routing problem called the platooning problem. We prove that this problem is NP-hard, even when the graph used to represent the road network is planar. We present integer linear programming formulations for instances of the platooning problem where deadlines are discarded, which we call the unlimited platooning problem. These allow us to calculate fuel-optimal solutions to the platooning problem for large-scale, real-world examples. The problems solved are orders of magnitude larger than problems previously solved exactly in the literature. We present several heuristics and compare their performance with the optimal solutions on the German Autobahn road network. The proposed heuristics find optimal or near-optimal solutions in most of the problem instances considered, especially when a final local search is applied. Assuming a fuel reduction factor of 10% from platooning, we find fuel savings from platooning of 1–2% for as few as 10 trucks in the road network; the percentage of savings increases with the number of trucks. If all trucks start at the same point, savings of up to 9% are obtained for only 200 trucks.
Bibliografia:AC02-06CH11357
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
ISSN:0968-090X
1879-2359
1879-2359
DOI:10.1016/j.trc.2015.08.019