Visualizations for Decision Support in Scenario-based Multiobjective Optimization

Babooshka Shavazipour, Manuel López-Ibáñez, Kaisa Miettinen

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalInformation Sciences
Volume578
DOIs
Publication statusAccepted/In press - 3 Jul 2021

Keywords

  • Empirical attainment function
  • MCDM
  • Multi-dimensional visualization
  • Scenario planning
  • Scenario-based multi-criteria optimization
  • Uncertainty

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