Sohail Razzaq
Efficient optimization techniques for resource allocation in UAVs mission framework
Razzaq, Sohail; Xydeas, Costas; Mahmood, Anzar; Ahmed, Saeed; Ratyal, Naeem Iqbal; Iqbal, Jamshed
Authors
Costas Xydeas
Anzar Mahmood
Saeed Ahmed
Naeem Iqbal Ratyal
Dr Jamshed Iqbal J.Iqbal@hull.ac.uk
Senior Lecturer
Abstract
This paper considers the generic problem of a central authority selecting an appropriate subset of operators in order to perform a process (i.e. mission or task) in an optimized manner. The subset is selected from a given and usually large set of 'n' candidate operators, with each operator having a certain resource availability and capability. This general mission performance optimization problem is considered in terms of Unmanned Aerial Vehicles (UAVs) acting as firefighting operators in a fire extinguishing mission and from a deterministic and a stochastic algorithmic point of view. Thus the applicability and performance of certain computationally efficient stochastic multistage optimization schemes is examined and compared to that produced by corresponding deterministic schemes. The simulation results show acceptable accuracy as well as useful computational efficiency of the proposed schemes when applied to the time critical resource allocation optimization problem. Distinguishing features of this work include development of a comprehensive UAV firefighting mission framework, development of deterministic as well as stochastic resource allocation optimization techniques for the mission and development of time-efficient search schemes. The work presented here is also useful for other UAV applications such as health care, surveillance and security operations as well as for other areas involving resource allocation such as wireless communications and smart grid.
Citation
Razzaq, S., Xydeas, C., Mahmood, A., Ahmed, S., Ratyal, N. I., & Iqbal, J. (2023). Efficient optimization techniques for resource allocation in UAVs mission framework. PLoS ONE, 18(4), Article e0283923. https://doi.org/10.1371/journal.pone.0283923
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 21, 2023 |
Online Publication Date | Apr 6, 2023 |
Publication Date | Apr 1, 2023 |
Deposit Date | Apr 28, 2023 |
Publicly Available Date | May 2, 2023 |
Journal | PloS one |
Print ISSN | 1932-6203 |
Electronic ISSN | 1932-6203 |
Publisher | Public Library of Science |
Peer Reviewed | Peer Reviewed |
Volume | 18 |
Issue | 4 |
Article Number | e0283923 |
DOI | https://doi.org/10.1371/journal.pone.0283923 |
Public URL | https://hull-repository.worktribe.com/output/4271206 |
Files
Published article
(2.3 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0
Copyright Statement
Copyright: © 2023 Razzaq et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.