TY  - JOUR
AV  - none
ID  - scholars3577
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878881939&doi=10.3233%2fIFS-130651&partnerID=40&md5=194d36dcf69eb9f1de25d17730a26dd4
KW  - Degree of satisfaction; Fuzzy optimization; Multi objective evolutionary algorithms; Multi-objective evolutionary; NSGA-II; Optimization environments; Posteriori decision; Production Planning
KW  -  Decision making; Evolutionary algorithms; Multiobjective optimization; Optimal systems; Pareto principle; Production control; Production engineering
KW  -  Planning
IS  - 2
JF  - Journal of Intelligent and Fuzzy Systems
VL  - 25
SN  - 10641246
TI  - A multi-objective evolutionary approach for fuzzy optimization in production planning
EP  - 455
SP  - 441
N2  - In this paper we propose a multi-objective optimization approach to solve nonlinear fuzzy optimization problems. Solutions in the Pareto front correspond with the fuzzy solution of the former fuzzy problem expressed in terms of the group of three parameters (x*, μ, α), i.e., optimal solution - degree of satisfaction - vagueness factor. The decision maker could choose, in a posteriori decision environment, the most convenient optimal solution according to his degree of satisfaction and vagueness factor. Additionally, an ad-hoc Pareto-based multi-objective evolutionary algorithm, ENORA-II, is proposed and validated in a production planning optimization environment. A real-world industrial problem for product-mix selection involving 8 decision variables and 21 constraints with fuzzy coefficients is considered as case study. ENORA-II has been evaluated with the existing methodologies in the field and results have been compared with the well-known multi-objective evolutionary algorithm NSGA-II. © 2013 - IOS Press and the authors. All rights reserved.
A1  - Jiménez, F.
A1  - Sánchez, G.
A1  - Vasant, P.
Y1  - 2013///
N1  - cited By 41
ER  -