Generative optimisation of resilient drone logistic networks

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57 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages8
ISBN (Electronic)9781665467087
DOIs
Publication statusPublished - 6 Sept 2022
EventIEEE World Congress on Computational Intelligence 2022 - Padua Congress Center, Padua, Italy
Duration: 18 Jul 202223 Jul 2022
https://wcci2022.org/

Publication series

Name2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings

Conference

ConferenceIEEE World Congress on Computational Intelligence 2022
Abbreviated titleIEEE WCCI
Country/TerritoryItaly
CityPadua
Period18/07/2223/07/22
Internet address

Keywords

  • physarum optimisation
  • digital twin
  • drone logistic network
  • vehicle routing problem
  • complex system
  • graph theory
  • resilience

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