RTXPS can trigger, as part of its repertoire of ACTIONs, complex simulation and optimization models.
These models can be run "embedded", as foreground functions, or as batch jobs in the background.
Communication and coordination between the models and the RTXPS inference engine is through the Dynamic Knowledge Base, that provides model inputs, and manages model outputs, interpreted and aggregated as DESCRIPTORS that the RTXPS RULES can process.
The basic models types available include:
The models generate an expected or desired (optimal) state of the system to guide the control options or drive communication (warning, alerts, alarms) where applicable. This makes it possible to use pro-active response based on forecast rather than reactive feedback strategies only.
The modular architecture of RTXPS can integrate a wide range of optimization algorithms, includsing "claasical" linear and dynamic programming, DO (discrete optimization), NLP (non-linear programmin), etc but also heuristic methods of CBR (Case Based Reasoning).
The primary toll is a two-step optimization and DSS approach, that combines
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