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.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Navigation in multiobjective optimization methods
AU - Allmendinger, Richard
AU - Ehrgott, Matthias
AU - Gandibleux, Xavier
AU - Geiger, Martin Josef
AU - Klamroth, Kathrin
AU - Luque, Mariano
N1 - 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.
PY - 2017/3/16
Y1 - 2017/3/16
N2 - 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.
AB - 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.
KW - Multiobjective optimization
KW - multiple criteria decision making
KW - preference learning
KW - navigation
U2 - 10.1002/mcda.1599
DO - 10.1002/mcda.1599
M3 - Journal article
VL - 24
SP - 57
EP - 70
JO - Journal of Multi-Criteria Decision Analysis
JF - Journal of Multi-Criteria Decision Analysis
SN - 1057-9214
IS - 1-2
ER -