Congestion-aware Bi-modal Delivery Systems Utilizing Drones

TL;DR

This paper develops a mathematical model for bi-modal delivery systems using trucks and drones, aiming to optimize routing to reduce road congestion and delivery latency, demonstrating the potential benefits of drone integration.

Contribution

It introduces a convex quadratic optimization framework that jointly considers societal costs and delivery efficiency for drone-truck delivery systems.

Findings

Drones can significantly reduce road congestion.

The proposed model efficiently scales to realistic scenarios.

Simulation shows improved delivery times with drone integration.

Abstract

Bi-modal delivery systems are a promising solution to the challenges posed by the increasing demand of e-commerce. Due to the potential benefit drones can have on logistics networks such as delivery systems, some countries have taken steps towards integrating drones into their airspace. In this paper we aim to quantify this potential by developing a mathematical model for a Bi-modal delivery system composed of trucks and drones. We propose an optimization formulation that can be efficiently solved in order to design socially-optimal routing and allocation policies. We incorporate both societal cost in terms of road congestion and parcel delivery latency in our formulation. Our model is able to quantify the effect drones have on mitigating road congestion, and can solve for the path routing needed to minimize the chosen objective. To accurately capture the effect of stopping trucks on road latency, we model it using SUMO by simulating roads shared by trucks and cars. Based on this, we show that the proposed framework is computationally feasible to scale due to its reliance on convex quadratic optimization techniques.