Vehicle Routing Problems in the Age of Semi-Autonomous Driving

TL;DR

This paper introduces the Vehicle Routing Problem in semi-autonomous environments, proposing a MILP model and a two-phase algorithm to optimize routing costs amid partial AV infrastructure, showing significant cost savings.

Contribution

It formulates the VRP-SA with a novel MILP model and develops an efficient two-phase algorithm tailored for semi-autonomous urban transportation networks.

Findings

Up to 37.5% reduction in routing costs with AV-enabled roads.

Cost savings increase with more budget and longer operation hours.

New benchmark instances tailored for VRP-SA testing.

Abstract

We are in the midst of a semi-autonomous era in urban transportation in which varying forms of vehicle autonomy are gradually being introduced. This phase of partial autonomy is anticipated by some to span a few decades due to various challenges, including budgetary constraints to upgrade the infrastructure and technological obstacles in the deployment of fully autonomous vehicles (AV) at scale. In this study, we introduce the vehicle routing problem in a semi-autonomous environment (VRP-SA) where the road network is not fully AV-enabled in the sense that a portion of it is either not suitable for AVs or requires additional resources in real-time (e.g., remote control) for AVs to pass through. Moreover, such resources are scarce and usually subject to a budget constraint. An exact mixed-integer linear program (MILP) is formulated to minimize the total routing cost of service in this environment. We propose a two-phase algorithm based on a family of feasibility recovering sub-problems (FRP) to solve the VRP-SA efficiently. Our algorithm is implemented and tested on a new set of instances that are tailored for the VRP-SA by adding stratified grid road networks to the benchmark instances. The result demonstrates a reduction of up to 37.5% in vehicle routing costs if the fleet actively exploits the AV-enabled roads in the environment. Additional analysis reveals that cost reduction is higher with more budget and longer operational hours.