The process of responding to unforeseen disturbances occurring during the execution of scheduled operations is called disruption managment (DM): Central aim is to select appropriate repair actions, derected at the minimization of the negative impacts typically associated with a disruption. As DM might represent a highly comlex task, it is desirable to support responsible process managers by means of Information Technology and Artificial Intelligence. This book therefore describes a comprehensive approach for the automated provision of decision support in the context of process disruption management. First, a conceptual framework for the formal description of DM problems is introduced. Then it is illustrated how this framework can be applied in the development and operation of Decision Support Systems (DSS). An evolutionary algorithm is presented for the optimization of disrupted schedules. Finally, the book shows how the concept of local optimization can be applied in order to handle even large problem instances within very short time.part from the technical realization, also organizational aspects which have to be considered for the introduction of a DSS are discussed.