George, Biju, Loo, Jonathan ORCID: https://orcid.org/0000-0002-2197-8126 and Jie, Wei ORCID: https://orcid.org/0000-0002-5392-0009 (2023) Novel multi-objective optimisation for maintenance activities of floating production storage and offloading facilities. Applied Ocean Research, 130. p. 103440. ISSN 0141-1187

Preview	PDF 1-s2.0-S0141118722003698-main.pdf - Published Version Available under License Creative Commons Attribution. Download (7MB) | Preview
Preview	PDF Novel multi-objective optimisation for maintenance of FPSOs_submitted.pdf - Accepted Version Download (1MB) | Preview

Abstract

An investigation of the recent advancements in modelling and optimisation techniques to develop maintenance strategies for offshore floating systems have been carried out in this paper and identified that the impact of time required to carry out activities have not been considered as an influencing factor in any of the existing formulations reviewed. The influence of time required to complete the activity, on the prioritisation of activities have been demonstrated in this work by means of a novel optimisation problem formulation for Floating Production Storage and Offloading Facility (FPSO) that maximises maintenance personnel resource utilisation and enables FPSO condition enhancement. To find the Pareto-optimal solution, an overall objective function has been developed considering the priorities with respect to design features, operating conditions, deteriorations, and the consequences of not doing the maintenance, taking into consideration the personnel resource time required for activity completion. This formulation provides flexibility to direct the focus of the overall objective function towards any one or more of the objective functions by adjusting their respective weight according to the maintenance strategy followed, which would supplement Regulatory oversight requirements of the FPSO.

Item Type:	Article
Identifier:	10.1016/j.apor.2022.103440
Keywords:	Maintenance, offshore system, planning, multi-objective optimisation, machine learning
Subjects:	Computing > Intelligent systems
Related URLs:	Publisher Publisher
Depositing User:	Jonathan Loo
Date Deposited:	07 Dec 2022 14:53
Last Modified:	04 Nov 2024 11:20
URI:	https://repository.uwl.ac.uk/id/eprint/9677

Downloads

Actions (login required)

View Item