A1 Journal article (refereed)
LR-NIMBUS : an interactive algorithm for uncertain multiobjective optimization with lightly robust efficient solutions (2022)

Koushki, J., Miettinen, K., & Soleimani-damaneh, M. (2022). LR-NIMBUS : an interactive algorithm for uncertain multiobjective optimization with lightly robust efficient solutions. Journal of Global Optimization, 83(4), 843-863. https://doi.org/10.1007/s10898-021-01118-8

JYU authors or editors

Publication details

All authors or editors: Koushki, Javad; Miettinen, Kaisa; Soleimani-damaneh, Majid

Journal or series: Journal of Global Optimization

ISSN: 0925-5001

eISSN: 1573-2916

Publication year: 2022

Publication date: 03/02/2022

Volume: 83

Issue number: 4

Pages range: 843-863

Publisher: Springer Science and Business Media LLC

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1007/s10898-021-01118-8

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/79666

Abstract

In this paper, we develop an interactive algorithm to support a decision maker to find a most preferred lightly robust efficient solution when solving uncertain multiobjective optimization problems. It extends the interactive NIMBUS method. The main idea underlying the designed algorithm, called LR-NIMBUS, is to ask the decision maker for a most acceptable (typical) scenario, find an efficient solution for this scenario satisfying the decision maker, and then apply the derived efficient solution to generate a lightly robust efficient solution. The preferences of the decision maker are incorporated through classifying the objective functions. A lightly robust efficient solution is generated by solving an augmented weighted achievement scalarizing function. We establish the tractability of the algorithm for important classes of objective functions and uncertainty sets. As an illustrative example, we model and solve a robust optimization problem in stock investment (portfolio selection).

Keywords: algorithms; decision making; optimisation; multi-objective optimisation; uncertainty; interactivity; scenarios; methods; mathematical methods; portfolios; securities portfolios

Free keywords: uncertain multiple criteria optimization; robust optimization; interactive methods; light robust efficiency; portfolio selection

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 2