Matteo Giuliani

TL;DR: Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

TL;DR: This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain and contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments.

TL;DR: This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EModPS solution design as well as of the resulting Pareto-approximate operating policies.

TL;DR: A conceptual framework describing when relative robustness values of decision alternatives obtained using different metrics are likely to agree and disagree is introduced, used as a measure of how “stable” the ranking of decision alternative is when determined using different robustness metrics.

TL;DR: In this article, a decision analytic framework is proposed to overcome policy inertia and myopia in complex river basin management contexts, which combines reservoir policy identification, many-objective optimization under uncertainty, and visual analytics to characterize current operations and discover key trade-offs between alternative policies for balancing competing demands and system uncertainties.