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Scope and objectives 

Uncertainty and risk are pervasive issues in planning and decision making tasks. With a wide range of causes and types of uncertainty, there are correspondingly many approaches to their treatment in decision analysis and optimization models. Some are tackled through discussion and creativity techniques to help decision makers set the boundaries of their problem; others are tackled through modelling techniques, e.g. probability, to reflect the randomness in the external world; yet others are approached through the use of sensitivity and robustness studies to explore the possible consequences of lack of precision in data estimates and judgments.

Different research communities address uncertainty issues in planning and decision making using different approaches, which often present similarities although being developed under distinct perspectives. There is a clear need for more work in the interfaces between these approaches for dealing creatively and effectively with different types of uncertainty in different contexts, also having in mind real-world applications.

This Conference is aimed at bringing together the specific expertise in aspects of handling uncertainty within decision support models to build a more comprehensive overview and integrated methodologies to tackle the various sources and types of uncertainties at stake in optimization and decision problems. The Conference will provide a forum in which researchers coming from different scientific disciplines and areas can discuss and share their experience regarding methodological approaches to tackle uncertainty for obtaining robust conclusions in decision support models with application to several areas.

Contributions from decision theory, Bayesian analysis, fuzzy sets, rough sets, risk analysis, stochastic programming, sensitivity analysis, robustness analysis, interval programming, inexact programming, constraint programming, evolutionary algorithms and meta-heuristics, multi-criteria analysis and multi-objective optimization, among others, are expected both from methodological and application perspectives, thus paving the way for a cross-fertilization between distinct ways to incorporate the treatment of uncertainty in optimization and decision support models.