An experience curve-based decision support model for prioritizing restoration needs of cultural heritage

Today's restoration and preservation of cultural heritage is an important task because of its historical significance, symbolism, and economic benefits. Decision makers or executors often encounter with taking decisions on which heritage is prioritized to be restored within the limited budget. However, very few tools are available to determine appropriately restoration priorities for the diverse historical heritages, perhaps because of a lack of systematized decision-making aids. This paper proposes an alternative decision support model to prioritize restoration needs within the executable budget. The model is constructed on stochastic analytic hierarchy process (S-AHP) and knowledge-based experience curve (EC); the former requires the input data to be random variables for interpreting probabilistically the ranks of the prioritized heritages and the latter reflects quantitatively the contribution of experts' knowledge to weighting significant criteria in carrying out an assessment of restoration urgency. The application of 14 cultural heritages in Korea has been conducted, and the results are analyzed to illustrate the model's efficiency.