Problem
Research questions and friction points this paper is trying to address.
Vehicle Platooning
Sequencing
Resequencing
Energy Consumption
Computational Complexity
Vehicle Platooning
🤖 AI Summary
This study addresses the problem of vehicle platoon ordering and reordering in road networks, with the objective of minimizing either total energy consumption or the maximum energy consumption of any single vehicle. For varying road segment lengths and under both full and partial information settings, the work provides the first comprehensive computational complexity classification for common vehicle–road configurations. For NP-hard cases, the authors develop polynomial-time algorithms, a fully polynomial-time approximation scheme (FPTAS), theoretically bounded heuristics, and efficient algorithms tailored to constrained reordering scenarios. The proposed heuristics yield solutions within 1% of optimality on average and demonstrate high efficiency along with provable performance guarantees, even under limited information availability and restricted positional adjustments.
📝 Abstract
Vehicle platooning offers significant benefits, including reduced energy consumption, lower emissions, improved road utilization, enhanced safety, and reduced driver fatigue. As intelligent driving technologies continue to advance, platoon sizes are expected to increase substantially, making the efficient sequencing and resequencing of vehicles increasingly important. We study the vehicle platoon sequencing and resequencing problem on road networks with varying segment lengths under two fundamental objectives: minimizing total energy consumption and minimizing the maximum energy consumption of any vehicle. For the typically encountered combinations of vehicle and road characteristics, we provide a complete computational complexity classification, either developing polynomial-time algorithms or proving computational intractability. For several intractable cases, we design fully polynomial-time approximation schemes and polynomial-time heuristics with provable performance guarantees. A computational study demonstrates that the proposed heuristics achieve average solutions within 1\% of optimal. We also consider settings in which only limited information about position-dependent energy savings is available and develop a heuristic with bounded worst-case performance. In addition, we present an efficient algorithm for on-road vehicle resequencing when only limited position changes are permitted. Together, these results provide a comprehensive algorithmic framework for energy-efficient vehicle platoon sequencing and resequencing.
Innovation
Methods, ideas, or system contributions that make the work stand out.
vehicle platooning
energy-efficient sequencing
computational complexity
approximation algorithms
resequencing heuristic
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