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.

本文提出了一种用于多场景多目标稳健决策的新优化方法，以及一种在任何解决方案生成之前进行场景发现和识别易受攻击场景的替代方法。为了演示和测试这种新颖的方法，我们使用了经典的浅湖问题。我们将使用新方法获得的结果与使用以前使用的方法获得的结果进行比较。我们表明，新方法保证了在所有选定场景下生成的解决方案的可行性和稳健效率，同时降低了计算成本，解决了场景依赖性问题，并使决策者能够探索最优/可行性之间的权衡任何选定的场景和更广泛场景的稳健性。