An adapted savings algorithm for planning heterogeneous logistics with uncrewed aerial vehicles
An adapted savings algorithm for planning heterogeneous logistics with uncrewed aerial vehicles
This paper proposes a new extension to the Sustainable Specimen Collection Problem (SSCP), where medical specimens are transported by vans, bikes, and uncrewed aerial vehicles (UAVs, or drones) from local medical practices/offices to a central hospital laboratory for analysis, employing a two-echelon collection approach. Time restrictions from existing operations and literature are also introduced, with the study being formulated as a weighted multi-objective problem seeking to minimise (i) operating costs; (ii) transit times; and (iii) energy/environmental impacts. A new adaptation of the Clarke and Wright Savings Algorithm is subsequently presented to create collection rounds that leverage each mode’s strengths. Subsequently, routes are compiled into workable fixed shifts using a modified bin-packing algorithm in each iteration.
The approach of this study is based on a case study of the UK’s National Health Service (NHS), involving the collection of pathology samples using traditional vans operating within fixed time slots. Using case study data from the Solent region (England), a novel test instance generation methodology was also developed, whereby realistic site positioning and origin-destination travel data are captured to enable effective algorithm experimentation. The findings from applying the proposed algorithm to a set of test instances based on this methodology are subsequently discussed, where it was found that the adapted savings and bin-packing approach produced effective solutions quickly, with 90% of all large instances (200 sites) being solved within 15 minutes. Further algorithm developments and the application of the devised problem/methodologies are also discussed.
Oakey, Andy
dfd6e317-1e6d-429c-a3e0-bc80e92787d1
Martinez-Sykora, Toni
2f9989e1-7860-4163-996c-b1e6f21d5bed
Cherrett, Tom
e5929951-e97c-4720-96a8-3e586f2d5f95
Oakey, Andy
dfd6e317-1e6d-429c-a3e0-bc80e92787d1
Martinez-Sykora, Toni
2f9989e1-7860-4163-996c-b1e6f21d5bed
Cherrett, Tom
e5929951-e97c-4720-96a8-3e586f2d5f95
Oakey, Andy, Martinez-Sykora, Toni and Cherrett, Tom
(2024)
An adapted savings algorithm for planning heterogeneous logistics with uncrewed aerial vehicles.
Multimodal Transportation.
(doi:10.1016/j.multra.2024.100170).
Abstract
This paper proposes a new extension to the Sustainable Specimen Collection Problem (SSCP), where medical specimens are transported by vans, bikes, and uncrewed aerial vehicles (UAVs, or drones) from local medical practices/offices to a central hospital laboratory for analysis, employing a two-echelon collection approach. Time restrictions from existing operations and literature are also introduced, with the study being formulated as a weighted multi-objective problem seeking to minimise (i) operating costs; (ii) transit times; and (iii) energy/environmental impacts. A new adaptation of the Clarke and Wright Savings Algorithm is subsequently presented to create collection rounds that leverage each mode’s strengths. Subsequently, routes are compiled into workable fixed shifts using a modified bin-packing algorithm in each iteration.
The approach of this study is based on a case study of the UK’s National Health Service (NHS), involving the collection of pathology samples using traditional vans operating within fixed time slots. Using case study data from the Solent region (England), a novel test instance generation methodology was also developed, whereby realistic site positioning and origin-destination travel data are captured to enable effective algorithm experimentation. The findings from applying the proposed algorithm to a set of test instances based on this methodology are subsequently discussed, where it was found that the adapted savings and bin-packing approach produced effective solutions quickly, with 90% of all large instances (200 sites) being solved within 15 minutes. Further algorithm developments and the application of the devised problem/methodologies are also discussed.
Text
Adapted_C_W_Heterogeneous_UAV_Paper
- Accepted Manuscript
Text
1-s2.0-S2772586324000510-main
- Proof
More information
Accepted/In Press date: 2 August 2024
e-pub ahead of print date: 12 September 2024
Identifiers
Local EPrints ID: 494129
URI: http://eprints.soton.ac.uk/id/eprint/494129
ISSN: 2772-5863
PURE UUID: a5b58fa7-6749-47f5-9245-34d30cb3d8fe
Catalogue record
Date deposited: 24 Sep 2024 16:47
Last modified: 01 Oct 2024 04:02
Export record
Altmetrics
Contributors
Author:
Andy Oakey
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics