 A robust optimization approach with probe-able uncertaintyChungmok Lee European Journal of Operational Research, 2022, vol. 296, issue 1, 218-239 Abstract: We consider optimization problems whose objective functions have uncertain coefficients. We assumed that, initially, the uncertain data are given as ranges, and probing of the true values of data is possible. The complete probing of all uncertain data will yield the true optimal solution. However, probing all uncertain data is undesirable because each probing may require cost or time depending on the methods of probing. We are interested in finding a solution, which we call Γ-optimal, that is guaranteed to remain the best solution even after additional Γ probings of uncertain data. An iterative algorithm to find the Γ-optimal solution is developed with theoretical probability bounds of the Γ-optimal solution being true optimal. Special algorithms are also developed for the problems on networks. The extensive computational study shows that the proposed approach could find the true optimal solutions at very high percentages, even with small numbers of probings. Keywords: Uncertainty modelling; Robust optimization; Mathematical programming; Explorable uncertainty (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:296:y:2022:i:1:p:218-239 DOI: 10.1016/j.ejor.2021.06.064 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.