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Decentralized coordination for truck platooning
Author(s) -
Zeng Yikai,
Wang Meng,
Rajan Raj Thilak
Publication year - 2022
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12899
Subject(s) - platoon , truck , scalability , computer science , computation , mathematical optimization , function (biology) , integer programming , constraint (computer aided design) , distributed computing , operations research , engineering , automotive engineering , algorithm , control (management) , mathematics , mechanical engineering , database , artificial intelligence , evolutionary biology , biology
Coordination for truck platooning refers to the active formation of a group of heavy‐duty vehicles traveling at close spacing to reduce the overall truck operations costs. Conventionally, this coordination is achieved by optimizing various truck‐related parameters, such as schedules, velocities, and routes, based on an objective function that minimizes a certain cost, for example, fuel usage. However, prevalent algorithms for the coordination problem are typically integer‐constrained, which are not only hard to solve but are not readily scalable to increasing fleet sizes and networks. In this paper, to overcome these limitations, we propose a centralized formulation to optimize the truck parameters and solve a multidimensional objective cost function including fuel, operation time costs and preferential penalty. Furthermore, to improve the scalability of our proposed approach, we propose a decentralized algorithm for the platoon coordination problem involving multiple fleets and objectives. We perform both theoretical and numerical studies to evaluate the performance of our decentralized algorithm against the centralized solution. Our analysis indicates that the computation time of the proposed decentralized algorithms is invariant to the increasing fleet size, at the cost of a small relative gap to the optimum cost given by the centralized method. We discuss these results and present future directions for research.