Multi-scenario multi-objective robust optimization under deep uncertainty : A posteriori approach
Shavazipour, B., Kwakkel, J. H., & Miettinen, K. (2021). Multi-scenario multi-objective robust optimization under deep uncertainty : A posteriori approach. Environmental modelling and software, 144, Article 105134. https://doi.org/10.1016/j.envsoft.2021.105134
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Environmental modelling and softwareDate
2021Discipline
Multiobjective Optimization GroupLaskennallinen tiedePäätöksen teko monitavoitteisestiMultiobjective Optimization GroupComputational ScienceDecision analytics utilizing causal models and multiobjective optimizationCopyright
© 2021 The Authors
This paper proposes a novel optimization approach for multi-scenario multi-objective robust decision making, as well as an alternative way for scenario discovery and identifying vulnerable scenarios even before any solution generation. To demonstrate and test the novel approach, we use the classic shallow lake problem. We compare the results obtained with the novel approach to those obtained with previously used approaches. We show that the novel approach guarantees the feasibility and robust efficiency of the produced solutions under all selected scenarios, while decreasing computation cost, addresses the scenario-dependency issues, and enables the decision-makers to explore the trade-off between optimality/feasibility in any selected scenario and robustness across a broader range of scenarios. We also find that the lake problem is ill-suited for reflecting trade-offs in robust performance over the set of scenarios and Pareto optimality in any specific scenario, highlighting the need for novel benchmark problems to properly evaluate novel approaches.
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Elsevier BVISSN Search the Publication Forum
1364-8152Keywords
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https://converis.jyu.fi/converis/portal/detail/Publication/99131741
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Research Council of FinlandFunding program(s)
Research profiles, AoF; Academy Project, AoFAdditional information about funding
This research was partly funded by the Academy of Finland (grants no. 322221 and 311877). This research is related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO, jyu.fi/demo) of the University of Jyvaskyla.License
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