An improved decomposition-based heuristic for truck platooning

TL;DR

This paper enhances a decomposition-based heuristic for optimizing truck routes and schedules in platooning systems, leading to improved performance in fuel-efficient transportation.

Contribution

It introduces new formulations and heuristics within Luo and Larson's framework, improving iterative routing and scheduling for large-scale truck platooning optimization.

Findings

Better performance than existing heuristic under realistic settings

Effective handling of large instances with new scheduling heuristic

Improved routing and scheduling formulations enhance overall efficiency

Abstract

Truck platooning is a promising transportation mode in which several trucks drive together and thus save fuel consumption by suffering less air resistance. In this paper, we consider a truck platooning system for which we jointly optimize the truck routes and schedules from the perspective of a central platform. We improve an existing decomposition-based heuristic by Luo and Larson (2022), which iteratively solves a routing and scheduling problem, with a cost modification step after each scheduling run. We propose different formulations for the routing and the scheduling problem and embed these into Luo and Larson's framework, and we examine ways to improve their iterative process. In addition, we propose another scheduling heuristic to deal with large instances. The computational results show that our procedure achieves better performance than the existing one under certain realistic settings.