Publications Details
Publications Details



Author: Giovanni M. SECHI, Andrea SULIS and Paola ZUDDAS

Year: 2009

Publisher: European Water Resources Association (EWRA)


In water resources management optimization problems under climatic variability, uncertainty is mainly associated with the values attribution for hydrological exogenous inflows and demand pattern trends. Deterministic optimization models are inadequate to deal with these problems and traditional stochastic optimization models frequently cannot be used if there is insufficient statistical information to support the optimization model. In this paper the uncertainty is modelled by a scenario approach in a multistage environment analysis which can include different possible system configurations in a wide time horizon. A robust chance optimization model is then developed in order to obtain a so-called “barycentric value” with respect to decision variables.

The paper proposed a subsequent validation phase defined as “re-optimization”. The re-optimization step, based on the previously obtained barycentric value solution, allows reducing the consequences deriving from wrong decisions. Implementation of the scenario-optimization procedure has been developed and coded in an improved version of WARGI DSS (Sechi et al. 2000, 2003, 2007). WARGI performs the scenario analysis by identifying trends and essential features of water system on which to base a robust decision policy for systems
management. The improved version of WARGI can be linked to commercial solvers as well as to some free solvers for the mathematical problem coded using the MPS standard.

The improved WARGI DSS can be used for large dimension problems based on the open source philosophy, that exploits the speed of network simplex methods in order to obtain very efficient solutions to the scenario optimization problem. The application to a real water resource system in Sardinia - Italy, shows the usefulness of the proposed approach for water resources systems affected by a high level of uncertainty in data related to hydrological inputs. It appears that the used scenario optimization approach can be a promising alternative tool for large size optimization problems coming for complex real resource systems under climatic uncertainty.