Home > Research > Publications & Outputs > Navigation in multiobjective optimization methods

Electronic data

  • Manuscript_Final_7March016_2_

    Rights statement: This is the peer reviewed version of the following article: Allmendinger R, Ehrgott M, Gandibleux X, Geiger, MJ, Klamroth K, Luque M. Navigation in multiobjective optimization methods, J Multi-Crit Decis Anal, 2017;24:57–70. https://doi.org/10.1002/mcda.1599 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/mcda.1599/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

    Accepted author manuscript, 115 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Navigation in multiobjective optimization methods

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • Richard Allmendinger
  • Matthias Ehrgott
  • Xavier Gandibleux
  • Martin Josef Geiger
  • Kathrin Klamroth
  • Mariano Luque
Close
<mark>Journal publication date</mark>23/11/2016
<mark>Journal</mark>Journal of Multi-Criteria Decision Analysis
Issue number1-2
Volume24
Number of pages14
Pages (from-to)57-70
Publication StatusE-pub ahead of print
Early online date23/11/16
<mark>Original language</mark>English

Abstract

Building on previous work of the authors, this paper formally defines and reviews the first approach, referred to as navigation, towards a common understanding of search and decision making strategies to identify the most preferred solution among the Pareto set for a multiobjective optimization problem. In navigation methods, the decision maker interactively learns about the problem, while the decision support system learns about the preferences of the decision maker. This work introduces a detailed view on navigation leading to the identification of integral components and features. A number of different existing navigation methods are reviewed and characterized.
Finally, an overview of applications involving navigation is given, and promising future research directions are discussed.

Bibliographic note

This is the peer reviewed version of the following article: Allmendinger R, Ehrgott M, Gandibleux X, Geiger, MJ, Klamroth K, Luque M. Navigation in multiobjective optimization methods, J Multi-Crit Decis Anal, 2017;24:57–70. https://doi.org/10.1002/mcda.1599 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/mcda.1599/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.