Optimizer

Organizations are frequently designed and redesigned, often in efforts to improve performance or meet various managerial goals for coordination and communication. Such design is often done through the review of a few of options and the use of managerial and possibly personnel insight into how the new design might work. In contrast, we provide a systematic optimization based approach. In this approach, the user can pick one or more Dynamic Network Analysis (DNA) metrics and then use one or more of the available optimizers to find a design that more closely meets this ideal. The optimizer utilizes heuristic based optimization procedures to generate an optimized organizational design given a particular mission. DNA metrics, such as Communication Congruence, Resource Congruence, Cognitive Load, and Actual Workload, serve to define criteria. The Optimizer can perform multi-criteria optimization in order to improve several metrics simultaneously. Two optimization methods can be used Monte Carlo and Simulated Annealing, both of which are statistical methods of finding a global optimum. DNA metrics used in the optimizations are computed by ORA.