An Optimization Approach for Historical Building Restoration Planning

Abstract

In this study, an optimization model has been developed to plan the restoration works envisaged to be done in historical buildings. There are very few studies in the literature on modeling the restoration works of historical monuments and cultural heritages, and most of these studies are based on multi-criteria decision-making models that aim to prioritize the works to be done. Restoration of historical monuments is an expensive and long process. Considering the cultural, social and economic importance of the buildings under the assumption that the works under restoration will be closed to visitors, the importance of optimizing the restoration works is understood. However, no study has been found in the operations research literature that deals with this problem in the scope mentioned. Where the minimum set of actions required to correct defects in a building is termed a 'work', it can be assumed that there may be more than one 'work' due to different defects in a building. Restoration works need to be assigned to work packages and scheduled according to the limited budget allocated for the restoration of the works. Since the budget is limited, the entire budget allocated for a certain period is reserved for only one work package. That is, work packages cannot be executed in parallel, a new work package cannot be started before a work package is completed. It is conceivable that similar types of work could be done by the same teams. There are economic advantages for similar works to be done by the same teams for different historical buildings located at close distances to each other. Under the assumption that the building will remain closed to use/visit before all the restoration works on a work are completed, it can be aimed to complete the restoration works of the buildings as early as possible. In this context, with the developed mathematical model, it is aimed to assign the works to the work packages by taking into account their similarity and geographical proximity, thus making optimal assignment and scheduling under limited budget conditions.