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Including robustness considerations in the search phase of Many-Objective Robust Decision Making.

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Title: Including robustness considerations in the search phase of Many-Objective Robust Decision Making.
Authors: Eker, Sibel1,2 eker@iiasa.ac.at, Kwakkel, Jan H.2 j.h.kwakkel@tudelft.nl
Source: Environmental Modelling & Software. Jul2018, Vol. 105, p201-216. 16p.
Subject Terms: *Environmental research, Robust control, Integrated software, Decision making in environmental policy, Environmental monitoring software
Abstract: Many-Objective Robust Decision Making (MORDM) is a prominent model-based approach for dealing with deep uncertainty. MORDM has four phases: a systems analytical problem formulation , a search phase to generate candidate solutions, a trade-off analysis where different strategies are compared across many objectives, and a scenario discovery phase to identify the vulnerabilities. In its original inception, the search phase identifies optimal strategies for a single reference scenario for deep uncertainties, which may result in missing locally near-optimal, but globally more robust strategies. Recent work has addressed this issue by generating candidate strategies for multiple policy-relevant scenarios. In this paper, we incorporate a systematic scenario selection procedure in the search phase to consider both policy relevance and scenario diversity. The results demonstrate an increased tradeoff variety besides higher robustness, compared to the solutions found for a reference scenario. Future research can routinize multi-scenario search in MORDM with the aid of software packages. [ABSTRACT FROM AUTHOR]
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Database: GreenFILE
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Abstract:Many-Objective Robust Decision Making (MORDM) is a prominent model-based approach for dealing with deep uncertainty. MORDM has four phases: a systems analytical problem formulation , a search phase to generate candidate solutions, a trade-off analysis where different strategies are compared across many objectives, and a scenario discovery phase to identify the vulnerabilities. In its original inception, the search phase identifies optimal strategies for a single reference scenario for deep uncertainties, which may result in missing locally near-optimal, but globally more robust strategies. Recent work has addressed this issue by generating candidate strategies for multiple policy-relevant scenarios. In this paper, we incorporate a systematic scenario selection procedure in the search phase to consider both policy relevance and scenario diversity. The results demonstrate an increased tradeoff variety besides higher robustness, compared to the solutions found for a reference scenario. Future research can routinize multi-scenario search in MORDM with the aid of software packages. [ABSTRACT FROM AUTHOR]
ISSN:13648152
DOI:10.1016/j.envsoft.2018.03.029