Our research focuses on the problem of recovering from perturbations in large-scale schedules, specifically on the ability of a human-machine partnership to dynamicMly modify an airline schedule in response to unanticipated disruptions. This task is characterized by massive interdependencies and a large space of possible actions. Our approach is to apply both qualitative, knowledgeintensive techniques relying on a memory of stereotypical failures and appropriate recoveries, and quantitative techniques drawn from the Operations Research community’s work on scheduling. Our main scientific challenge is to represent schedules, failures and repairs so as to make both sets of techniques applicable to the same data. This paper outlines ongoing research in which we are cooperating with United Airlines to develop our understanding of the scientific issues underlying the practicalities of dynamic, real-time schedule repair.