Generative optimisation of resilient drone logistic networks

Tools

Filippi, Gianluca and Vasile, Massimiliano and Patelli, Edoardo and Fossati, Marco; (2022) Generative optimisation of resilient drone logistic networks. In: 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings . IEEE, ITA. ISBN 9781665467087 (https://doi.org/10.1109/CEC55065.2022.9870306)

[thumbnail of Filippi-etal-CEC2022-Generative-optimisation-of-resilient-drone-logistic-networks]
Preview
 Text. Filename: Filippi_etal_CEC2022_Generative_optimisation_of_resilient_drone_logistic_networks.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (873kB)| Preview

Abstract

This paper presents a novel approach to the gener-ative design optimisation of a resilient Drone Logistic Network (DLN) for the delivery of medical equipment in Scotland. A DLN is a complex system composed of a high number of different classes of drones and ground infrastructures. The corresponding DLN model is composed of a number of interconnected digital twins of each one of these infrastructures and vehicles, forming a single digital twin of the whole logistic network. The paper proposes a multi-agent bio-inspired optimisation approach based on the analogy with the Physarum Policefalum slime mould that incrementally generates and optimise the DLN. A graph theory methodology is also employed to evaluate the network resilience where random failures, and their cascade effect, are simulated. The different conflicting objectives are aggregated into a single global performance index by using Pascoletti-Serafini scalarisation.

ORCID iDs

Filippi, Gianluca, Vasile, Massimiliano ORCID logoORCID: https://orcid.org/0000-0001-8302-6465, Patelli, Edoardo ORCID logoORCID: https://orcid.org/0000-0002-5007-7247 and Fossati, Marco ORCID logoORCID: https://orcid.org/0000-0002-1165-5825;
CORE (COnnecting REpositories)