Abstract
Abstract The advantages of several methods for the steady-state control of large-scale systems are combined to give an algorithm which produces optimum solutions for a wide class of processes. It is demonstrated how optimum reality solutions can be obtained even when large model-reality differences exist. The method employs a hierarchical framework where coordination of local decision problems is achieved jointly by price and modifier variables. A three-subsystem process is optimized to illustrate the procedure.
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