TY - JOUR
T1 - Multi-scenario multi-objective robust optimization under deep uncertainty
T2 - A posteriori approach
AU - Shavazipour, Babooshka
AU - Kwakkel, Jan H.
AU - Miettinen, Kaisa
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Deep uncertainty
KW - Multi-objective optimization
KW - Reference points
KW - Robust decision making scalarizing functions
KW - Scenario planning
UR - http://www.scopus.com/inward/record.url?scp=85110643976&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2021.105134
DO - 10.1016/j.envsoft.2021.105134
M3 - Article
AN - SCOPUS:85110643976
VL - 144
JO - Environmental Modelling & Software
JF - Environmental Modelling & Software
SN - 1364-8152
M1 - 105134
ER -