Testing the potential of Multiobjective Evolutionary Algorithms (MOEAs) with Colorado water managers
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Testing the potential of Multiobjective Evolutionary Algorithms (MOEAs) with Colorado water managers

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  • Journal Title:
    Environmental Modelling & Software
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    Multiobjective Evolutionary Algorithms (MOEAs) generate quantitative information about performance relationships between a system's potentially conflicting objectives (termed tradeoffs). Research applications have suggested that evaluating tradeoffs can enhance long term water utility planning, but no studies have formally engaged with practitioners to assess their perceptions of tradeoffs generated by MOEAs. This article examines how practitioners interact with MOEA tradeoffs and reports their ideas for how their agencies could use MOEA results. We hosted a group of Colorado water managers at a charrette, or structured investigatory workshop, where they directly interacted with tradeoffs, discussed how they used the information, and linked their workshop experiences to opportunities for MOEAs to enhance their agencies' planning processes. Among other interesting results, we found that managers' portfolio preferences diverged as tradeoff information increased and that structured information about the relationships between decision levers and performance would be beneficial for interpreting tradeoffs.
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    Environmental Modelling & Software, 117, 149-163
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    Accepted Manuscript
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