Skip to Main content Skip to Navigation
Conference papers

β-Robustness approach for fuzzy multi-objective problems

Oumayma Bahri 1 El-Ghazali Talbi 2, 3 Nahla Benamor 1
1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : The paper addresses the robustness of multi-objective optimization problems with fuzzy data, expressed via triangular fuzzy numbers. To this end, we introduced a new robustness approach able to deal with fuzziness in the multi-objective context. The proposed approach is composed of two main contributions: First, new concepts of β-robustness are proposed to analyze fuzziness propagation to the multiple objectives. Second, an extension of our previously proposed evolutionary algorithms is suggested for integrating robustness. These proposals are illustrated on a multi-objective vehicle routing problem with fuzzy customer demands. The experimental results on different instances show the efficiency of the proposed approach.
Document type :
Conference papers
Complete list of metadata
https://hal.inria.fr/hal-01423494
Contributor : Talbi El-Ghazali <>
Submitted on : Thursday, December 29, 2016 - 8:56:45 PM
Last modification on : Friday, December 11, 2020 - 6:44:05 PM

Identifiers

Collections

CNRS | INRIA | CRISTAL | UNIV-LILLE

Citation

Oumayma Bahri, El-Ghazali Talbi, Nahla Benamor. β-Robustness approach for fuzzy multi-objective problems. IPMU‘2016 - 16th Int. Conference On Information Processing and Management on Uncertainty in Knowledge-based Systems, 2016, Eindhoeven, Netherlands. pp.225-237, ⟨10.1007/978-3-319-40581-0_19⟩. ⟨hal-01423494⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

293

 

Contact                    Legal Notice                    Personal Data