Optimizing Two-Truck Platooning with Deadlines

TL;DR

This paper presents a novel optimization framework for two-truck platooning with deadlines, providing an FPTAS and an efficient algorithm that significantly reduces fuel consumption while respecting constraints.

Contribution

It introduces a new formulation for two-truck platooning with deadlines, proves its weak NP-hardness, and develops an FPTAS and a practical dual-subgradient algorithm for large-scale problems.

Findings

FPTAS achieves near-optimal solutions with adjustable accuracy.

The dual-subgradient algorithm converges efficiently for large instances.

Simulations show up to 24% fuel savings over baseline methods.

Abstract

We study a transportation problem where two heavy-duty trucks travel across the national highway from separate origins to destinations, subject to individual deadline constraints. Our objective is to minimize their total fuel consumption by jointly optimizing path planning, speed planning, and platooning configuration. Such a two-truck platooning problem is pervasive in practice yet challenging to solve due to hard deadline constraints and enormous platooning configurations to consider. We first leverage a unique problem structure to significantly simplify platooning optimization and present a novel formulation. We prove that the two-truck platooning problem is weakly NP-hard and admits a Fully Polynomial Time Approximation Scheme (FPTAS). The FPTAS can achieve a fuel consumption within a ratio of $(1+\epsilon)$ to the optimal (for any $\epsilon>0$) with a time complexity polynomial in the size of the transportation network and $1/\epsilon$. These results are in sharp contrast to the general multi-truck platooning problem, which is known to be APX-hard and repels any FPTAS. As the FPTAS still incurs excessive running time for large-scale cases, we design an efficient dual-subgradient algorithm for solving large-/national- scale instances. It is an iterative algorithm that always converges. We prove that each iteration only incurs polynomial-time complexity, albeit it requires solving an integer linear programming problem optimally. We characterize a condition under which the algorithm generates an optimal solution and derive a posterior performance bound when the condition is not met. Extensive simulations based on real-world traces show that our joint solution of path planning, speed planning, and platooning saves up to $24\%$ fuel as compared to baseline alternatives.