Multicriteria approach for intelligent decision support in supervisory control

In the framework of integrated automation, this work concerns the top level of the management and supervision of complex automated systems. When a process is being disturbed, the supervisory function modifies the established production planning, in accordance with different norms and constraints. The operator remains beside the regulated process controls to perform manual operations. The number of potential actions and the conflicting nature of some objectives make his task complex: he must reach quantitative and qualitative objectives with imperfect and temporal information. To assist him, the authors study a decision support model following a multicriteria approach involving the supervision problem. AI techniques and DSS are used to develop the aid tool. The Spinning Reserve problem encountered by ‘Electricité de France’ is studied and used as support. To test the present concepts, the authors develop the CASTART experimental support based on a synergy between the user, the problem, and the resolution models.