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Intelligent mesh optical networks deployed today offer unparalleled capacity, flexibility, availability, and, inevitably, new challenges to master all these qualities in the most efficient and practical manner. More specifically, demands are routed according to the state of the network available at the moment. As the network and the traffic evolve, the lightpaths of the existing demands becomes sub-optimal. In this paper we study two algorithms to re-optimize lightpaths in resilient mesh optical networks. One is a complete re-optimization algorithm that re-routes both primary and backup paths, and the second is a partial re-optimization algorithm that re-routes the backup paths only. We show that on average, these algorithms allow bandwidth savings of 3% to 5% of the total capacity in scenarios where the backup path only is re-routed, and substantially larger bandwidth savings when both the working and backup paths are re-routed. We also prove that trying all possible demand permutations with an online algorithm does not guarantee optimality, and in certain cases does not achieve it, while for the same scenario optimality is achieved through re-optimization. This observation motivates the needs for a re-optimization approach that does not just simply look at different sequences, and we propose and experiment with such an approach. Re-optimization has actually been performed in a nationwide live optical mesh network and the resulting savings are reported in this paper, validating reality and the usefulness of re-optimization in real networks.