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\(\beta\)-robustness approach for fuzzy multi-objective problems. (English) Zbl 1455.90157
Carvalho, Joao Paulo (ed.) et al., Information processing and management of uncertainty in knowledge-based systems. 16th international conference, IPMU 2016, Eindhoven, The Netherlands, June 20–24, 2016. Proceedings. Part II. Cham: Springer. Commun. Comput. Inf. Sci. 611, 225-237 (2016).
Summary: 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 \(\beta\)-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.
For the entire collection see [Zbl 1385.68004].
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
90B06 Transportation, logistics and supply chain management
90C29 Multi-objective and goal programming
90C59 Approximation methods and heuristics in mathematical programming
Full Text: DOI
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