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 language | English |
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Title of host publication | 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Number of pages | 8 |
ISBN (Electronic) | 9781665467087 |
DOIs | |
Publication status | Published - 6 Sept 2022 |
Event | IEEE World Congress on Computational Intelligence 2022 - Padua Congress Center, Padua, Italy Duration: 18 Jul 2022 → 23 Jul 2022 https://wcci2022.org/ |
Publication series
Name | 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings |
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Conference
Conference | IEEE World Congress on Computational Intelligence 2022 |
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Abbreviated title | IEEE WCCI |
Country/Territory | Italy |
City | Padua |
Period | 18/07/22 → 23/07/22 |
Internet address |
Keywords
- physarum optimisation
- digital twin
- drone logistic network
- vehicle routing problem
- complex system
- graph theory
- resilience