LAUSR

ABSTRACT The aim of this study has been to develop a two-level multi-objective genetic algorithm (MOGA) to optimize risk-based LCC analysis to find the optimal maintenance replacement time for road tunnel ventilation fans. Level 1 uses a MOGA based on a financial risk model to provide different risk percentages, while level 2 uses a MOGA based on an LCC model to estimate the optimal fan replacement time. Our method is compared with the approach of using a risk-based LCC model. The results are promising, showing that the risk-based LCC offers the possibility of significantly reducing the maintenance costs of the ventilation system by optimising the replacement schedule by considering the risk costs. The risk-based LCC can be used with repairable components, making it applicable, useful and implementable within Swedish Transport Administration (Trafikverket). In this study, MOGA operators have selected the cost of maintenance and risk data through the previous levels using different ways to provide different possible solutions. A drawback of the MOGA based on a risk-based LCC model with regard to its estimation is that a late replacement period over 20-year period might increase the maintenance cost. Therefore, the MOGA does not provide a good solution for a risk-based LCC.