SHEPHERD : a digital blueprint for drone based logistic networks

Tools

Filippi, Gianluca and Patelli, Edoardo and Basu, Tathagata and Personage, Ben and Maddock, Christie and Fossati, Marco and Roper, Marc and Vasile, Massimiliano; Kołowrocki, Krzysztof and Magryta-Mut, Beata, eds. (2024) SHEPHERD : a digital blueprint for drone based logistic networks. In: 34th European Safety and Reliability Conference. Polish Safety and Reliability Association, Gdynia, pp. 77-86. ISBN 978-83-68136-18-0

[thumbnail of Filippi-etal-ARSS-ESREL-2024-Shepherd-a-digital-blueprint-for-drone-based-logistic-networks]
Preview
 Text. Filename: Filippi-etal-ARSS-ESREL-2024-Shepherd-a-digital-blueprint-for-drone-based-logistic-networks.pdf
Final Published Version
Download (1MB)| Preview

Abstract

Distributed health care network has been a key area of interest in improving the health care systems and with the recent developments of cost-effective unmanned autonomous vehicles (UAV), the use of drone for health care system is also gaining interest from different government organisations. Moving towards this direction, NHS (National Health Service) Scotland is also looking into the prospects of using drones for medical deliveries across Scotland for which we are interested in designing an optimal drone network. In general, to design such networks, we need to consider different quantities of interest such as flight time, capital expenditure, risk impact, etc. In this contribution, we are specifically interested in discussing the resilience of a drone logistic network which is important to ensure an interrupted chain of communication within and between different regional boards of NHS Scotland as well as smooth delivery of life saving medical objects. We treat the drone network as a graph and see how the graph behaves when failures happen due to uncertain events. We associate a probability interval to each basic event and compute the corresponding network efficiency (ϵi) after its verification. We calculate this efficiency as a combination of all the pairwise ’reachability’ within a pair of source and receiver locations within the network, given the state of the network. Network efficiency is a function of the expected time required by drones through optimal paths. We consider finally the possibility for nodes to recover after failure. This allows to quantify the reaction capability of the network after uncertain events. In this sense, resilience is the network ability to absorb shocks and recover after them. We then use this to illustrate our result for different scenarios to explain the use of our proposed resilience metric and also give a notion of 'trade-off' between the cost of network design and the network resilience to assist the decision makers.

ORCID iDs

Filippi, Gianluca, Patelli, Edoardo ORCID logoORCID: https://orcid.org/0000-0002-5007-7247, Basu, Tathagata ORCID logoORCID: https://orcid.org/0000-0002-6851-154X, Personage, Ben, Maddock, Christie ORCID logoORCID: https://orcid.org/0000-0003-1079-4863, Fossati, Marco ORCID logoORCID: https://orcid.org/0000-0002-1165-5825, Roper, Marc ORCID logoORCID: https://orcid.org/0000-0001-6794-4637 and Vasile, Massimiliano ORCID logoORCID: https://orcid.org/0000-0001-8302-6465; Kołowrocki, Krzysztof and Magryta-Mut, Beata
CORE (COnnecting REpositories)