Communication-aware Drone Delivery Problem

TL;DR

This paper introduces a communication-aware drone delivery problem that incorporates handover and outage constraints, optimizing delivery routes while ensuring communication quality, and proposes algorithms to solve large instances efficiently.

Contribution

It presents a novel formulation of the drone delivery problem that includes communication constraints and develops a genetic algorithm for large-scale problem solving.

Findings

Genetic Algorithm outperforms MIP solver on large instances.

Communication constraints significantly impact delivery route planning.

Proposed model effectively balances delivery efficiency and communication quality.

Abstract

The drone delivery problem (DDP) has been introduced to include aerial vehicles in last-mile delivery operations to increase efficiency. However, the existing studies have not incorporated the communication quality requirements of such a delivery operation. This study introduces the Communication-aware DDP (C-DDP), which incorporates handover and outage constraints. In particular, any trip of a drone to deliver a customer package must require less than a certain number of handover operations and cannot exceed a predefined outage duration threshold. The authors develop a Mixed Integer Programming (MIP) model to minimize the total flight distance while satisfying communication constraints as well as the time windows of customers. We present a Genetic Algorithm (GA) that can solve large instances, and compare its performance with an off-the-shelf MIP solver. Computational results show that the GA can outperform the MIP solver for solving larger instances and is a better option.