In recent years, a family of approaches has emerged for supporting decision-making on complex environmental problems characterised by deep uncertainties and competing priorities. Many-Objective Robust Decision Making (MORDM), Multi-scenario MORDM and. Many-Objective Robust Optimization (MORO) differ with respect to the degree to which robustness is considered during the search for promising candidate solutions. To assess the efficacy of these three methods, we compare them using three different policy formulations of the lake problem: inter-temporal, planned adaptive, and direct policy search. The more robustness is considered in the search phase, the more robust solutions are also after re-evaluation but also the lower the performance in individual reference scenarios. Adaptive policy formulations positively affect robustness, but do not reduce the price for robustness. Multi-scenario MORDM strikes a pragmatic balance between robustness considerations and optimality in individual scenarios, at reasonable computational costs.

近年来，出现了一系列方法来支持对具有严重不确定性和竞争重点的复杂环境问题的决策。多目标鲁棒决策（MORDM），多场景MORDM和。多目标鲁棒优化（MORO）在寻找有前途的候选解决方案期间考虑鲁棒性的程度方面有所不同。为了评估这三种方法的有效性，我们使用三种不同的湖泊问题政策表述对它们进行比较：跨时间，计划内自适应和直接政策搜索。在搜索阶段考虑的鲁棒性越强，重新评估后的解决方案也越鲁棒，但各个参考方案的性能也越低。自适应政策的制定会对稳健性产生积极影响，但不要为了健壮而降低价格。多方案MORDM以合理的计算成本在各个方案的鲁棒性和最优性之间取得了务实的平衡。