TY - JOUR
T1 - Visualizations for Decision Support in Scenario-based Multiobjective Optimization
AU - Shavazipour, Babooshka
AU - López-Ibáñez, Manuel
AU - Miettinen, Kaisa
N1 - Funding Information:
This research was partly funded by the Academy of Finland (Grants Nos. 287496 and 322221). This research is also related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO, jyu.fi/demo) of the University of Jyvaskyla. M.López-Ibáñez is a “Beatriz Galindo” Senior Distinguished Researcher (BEAGAL 18/00053) funded by the Spanish Ministry of Science and Innovation (MICINN).
Funding Information:
This research was partly funded by the Academy of Finland (Grants Nos. 287496 and 322221). This research is also related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO, jyu.fi/demo) of the University of Jyvaskyla. M.L?pez-Ib??ez is a ?Beatriz Galindo? Senior Distinguished Researcher (BEAGAL 18/00053) funded by the Spanish Ministry of Science and Innovation (MICINN).
Publisher Copyright:
© 2021 The Authors
PY - 2021/7/3
Y1 - 2021/7/3
N2 - We address challenges of decision problems when managers need to optimize several conflicting objectives simultaneously under uncertainty. We propose visualization tools to support the solution of such scenario-based multiobjective optimization problems. Suitable graphical visualizations are necessary to support managers in understanding, evaluating, and comparing the performances of management decisions according to all objectives in all plausible scenarios. To date, no appropriate visualization has been suggested. This paper fills this gap by proposing two visualization methods: a novel extension of empirical attainment functions for scenarios and an adapted version of heatmaps. They help a decisionmaker in gaining insight into realizations of trade-offs and comparisons between objective functions in different scenarios. Some fundamental questions that a decision-maker may wish to answer with the help of visualizations are also identified. Several examples are utilized to illustrate how the proposed visualizations support a decision-maker in evaluating and comparing solutions to be able to make a robust decision by answering the questions. Finally, we validate the usefulness of the proposed visualizations in a real-world problem with a real decision-maker. We conclude with guidelines regarding which of the proposed visualizations are best suited for different problem classes.
AB - We address challenges of decision problems when managers need to optimize several conflicting objectives simultaneously under uncertainty. We propose visualization tools to support the solution of such scenario-based multiobjective optimization problems. Suitable graphical visualizations are necessary to support managers in understanding, evaluating, and comparing the performances of management decisions according to all objectives in all plausible scenarios. To date, no appropriate visualization has been suggested. This paper fills this gap by proposing two visualization methods: a novel extension of empirical attainment functions for scenarios and an adapted version of heatmaps. They help a decisionmaker in gaining insight into realizations of trade-offs and comparisons between objective functions in different scenarios. Some fundamental questions that a decision-maker may wish to answer with the help of visualizations are also identified. Several examples are utilized to illustrate how the proposed visualizations support a decision-maker in evaluating and comparing solutions to be able to make a robust decision by answering the questions. Finally, we validate the usefulness of the proposed visualizations in a real-world problem with a real decision-maker. We conclude with guidelines regarding which of the proposed visualizations are best suited for different problem classes.
KW - Empirical attainment function
KW - MCDM
KW - Multi-dimensional visualization
KW - Scenario planning
KW - Scenario-based multi-criteria optimization
KW - Uncertainty
U2 - 10.1016/j.ins.2021.07.025
DO - 10.1016/j.ins.2021.07.025
M3 - Article
SN - 0020-0255
VL - 578
SP - 1
EP - 21
JO - Information Sciences
JF - Information Sciences
ER -