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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]
Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
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An: 129947966
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  Data: Including robustness considerations in the search phase of Many-Objective Robust Decision Making.
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  Data: *<searchLink fieldCode="DE" term="%22Environmental+research%22">Environmental research</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+software%22">Integrated software</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making+in+environmental+policy%22">Decision making in environmental policy</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring+software%22">Environmental monitoring software</searchLink>
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  Data: 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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  Data: <i>Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.envsoft.2018.03.029
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      – Code: eng
        Text: English
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      – SubjectFull: Environmental research
        Type: general
      – SubjectFull: Robust control
        Type: general
      – SubjectFull: Integrated software
        Type: general
      – SubjectFull: Decision making in environmental policy
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      – SubjectFull: Environmental monitoring software
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              Text: Jul2018
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