Interactive multiobjective optimization with NIMBUS for decision making under uncertainty

Kaisa Miettinen, Jyri Mustajoki, Theodor J. Stewart

Research output: Contribution to journalArticlepeer-review

Abstract

We propose an interactive method for decision making under uncertainty, where uncertainty is related to the lack of understanding about consequences of actions. Such situations are typical, for example, in design problems, where a decision maker has to make a decision about a design at a certain moment of time even though the actual consequences of this decision can be possibly seen only many years later. To overcome the difficulty of predicting future events when no probabilities of events are available, our method utilizes groupings of objectives or scenarios to capture different types of future events. Each scenario is modeled as a multiobjective optimization problem to represent different and conflicting objectives associated with the scenarios. We utilize the interactive classification-based multiobjective optimization method NIMBUS for assessing the relative optimality of the current solution in different scenarios. This information can be utilized when considering the next step of the overall solution process. Decision making is performed by giving special attention to individual scenarios. We demonstrate our method with an example in portfolio optimization. © 2013 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Pages (from-to)39-56
Number of pages17
JournalOR Spectrum
Volume36
Issue number1
Early online date2 Jun 2013
DOIs
Publication statusPublished - 2014

Keywords

  • Classification of objectives
  • Interactive methods
  • Multiple objective programming
  • Pareto optimality
  • Scenarios
  • Uncertainty handling

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