Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/131186
Title: Enhancing the maintenance strategy and cost in systems with surrogate assisted multiobjective evolutionary algorithms
Authors: Greiner Sánchez, David Juan 
Cacereño Ibáñez, Andrés 
UNESCO Clasification: 330506 Ingeniería civil
330411 Diseño de sistemas de calculo
Keywords: Availability
Digital Twin (Dt)
Evolutionary Algorithms (Ea)
Maintenance
Multiobjective Optimization, et al
Issue Date: 2024
Journal: Developments in the Built Environment 
Abstract: Digital twins need efficient methodologies to design maintenance strategies for decision-making purposes. Recently, a methodology coupling computational simulation and multiobjective evolutionary algorithms has been proposed for developing maintenance strategies consisting in assigning times for preventive maintenance activities and designing the layout of components of a system, minimizing the unavailability of the system and the strategy cost. Here, surrogate assisted evolutionary algorithms (SAEAs) enhance the multiobjective optimization and improve the drawback of the computational cost of the maintenance strategy assessment based on discrete simulation. Several Kriging surrogates were tested. Two industrial test cases are handled in the experimental section, where the methodology succeed in obtaining nondominated designs improving previous benchmarks, and enhancing state-of-the-art multiobjective optimizers, with up to an order of magnitude in terms of the number of fitness function evaluations. Results show that using multiobjective SAEAs in the development of optimal maintenance strategies could foster and improve digital twins operations.
URI: http://hdl.handle.net/10553/131186
DOI: 10.1016/j.dibe.2024.100478
Source: Developments in the Built Environment [EISSN 2666-1659], v. 19, (Octubre 2024)
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