Browse > Article
http://dx.doi.org/10.5516/NET.2010.42.4.414

OPTIMIZATION OF THE TEST INTERVALS OF A NUCLEAR SAFETY SYSTEM BY GENETIC ALGORITHMS, SOLUTION CLUSTERING AND FUZZY PREFERENCE ASSIGNMENT  

Zio, E. (Ecole Centrale Paris- Supelec Chair Systems Science and Energetic Challenge European Foundation for New Energy - EDF)
Bazzo, R. (Politecnico di Milano)
Publication Information
Nuclear Engineering and Technology / v.42, no.4, 2010 , pp. 414-425 More about this Journal
Abstract
In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.
Keywords
Multiobjective Optimization; Pareto Front; Pareto Set; Subtractive Clustering; Level Diagrams; Fuzzy Preference Assignment; Fuzzy Preference Scoring; Genetic Algorithms; Test Intervals Optimization; Safety System; Nuclear Power Plant;
Citations & Related Records

Times Cited By Web Of Science : 1  (Related Records In Web of Science)
Times Cited By SCOPUS : 4
연도 인용수 순위
1 Giuggioli Busacca, P., Marseguerra, M., Zio, E. (2001), Multiobjective Optimization by Genetic Algorithms: Application to Safety Systems, Reliability Engineering and System Safety, 72: 59-74   DOI
2 Katagiri, H., Sakawa, M, Kato, K., Nishizaki, I. (2008), Interactive multiobjective fuzzy random linear programming: Maximization of possibility and probability, European Journal of Operational Research, 188: 530-539   DOI
3 Malakooti, B. (1988) A decision support system and a heuristic interactive approach for solving discrete multiple criteria problems, IEEE Trans. On Sys., Man and Cyber., 18: 273-284.   DOI
4 Martorell, S., Carlos, S., Sanchez, A., Serradell, V. (2000) Constrained Optimization of Test Intervals Using a Steady-State Genetic Algorithm, Reliab, Engng Syst Safety, 67:215-32.   DOI
5 Molina, J., Santana, L.V., Hernandez-Diaz, A.G., Coello Coello, C.A., Caballero, R. (2009), g-dominance: Reference Point Based Dominance for Multiobjective Metaheuristics, European Journal of Operational Research, 197: 658-692.
6 NRC, US Nuclear Regulatory commission,. Rates of Initiating Events at United States Nuclear Power Plants: 1987-1995, NUREG/CR- 5750.
7 ICRP Publication 60, (1991), 1990 recommendations of the International Commission on Radiological Protection, Annals of the ICRP, 21: 1-3.
8 Rios Insua, D., Martin, J. (1994), Robustness Issue under Imprecise Beliefs and Preferences, Journal of Statistical Planning and Inference, 40, Issues 2-3: 383-389.   DOI
9 Rousseeuw, P. J. (1987) Silhouettes: A graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics, 20 : 53-65.   DOI   ScienceOn
10 Rousseeuw P., Trauwaert E. and Kaufman L. (1989), Some Silhouette-based Graphics for Clustering Interpretation. Belgian Journal of Operations Research, Statistics and Computer Science, 29 (3).
11 Blasco , X., Herrero, J.M., Sanchis, J., Martínez, M. (2008), A New Graphical Visualization of n-Dimensional Pareto Front for Decision-Making in Multiobjective Optimization, Information Science, 178: 3908-3924.   DOI
12 Chiu, S. (1994), Fuzzy Model Identification Based on Cluster Estimation, Journal of Intelligent & Fuzzy Systems, 2 (3)
13 Cho, K. I., Kim, S. H. (1997) An improved Interactive hybrid method for the linear multi-objective knapsack problema, Computers Ops. Res., 24, 11: 991-1003   DOI
14 De Boer, L., van der Wegen, L, Telgen, J. (1998), Outranking methods in support of supplier selection, European Journal of Purchasing and Supply Management, 4: 109-118.   DOI
15 Roy, B. (1968) Classement et Choix en Presence de Points de Vue Multiples (la Methode ELECTRE), RIRO, 8: 57-75.
16 Roy, B. (1974) Criteres Multiples et Modelisation des Preferences (l’Apport des Relations de Surclassement), Revue d’Economie Politique, 84: 1-44.
17 Roy, B., Bouyssou, D. (1986), Comparison of two Decision-Aid Models Applied to a Nuclear Power Plant Siting Example, European Journal of Operational Research, 25:200-215.   DOI
18 Yang, J.B. (1996), Multiple Criteria Decision Making Methods and Applications, Hunan Publishing House, Changsha P.R. China.
19 Yang, J-E., Hwang, M-J., Sung, T-Y., Jin, Y. (1999) Application of Genetic Algorithm for Reliability Allocation in Nuclear Power Plants, Reliab Engng Syst Safety, 65:229-38.   DOI
20 Yang, J.B. (2000), Minimax Reference Point Approach and its Application for Multiobjective Optimisation, European Journal of Operational Research, 126: 541-556.   DOI
21 Zio, E., Baraldi, P., Pedroni, N. (2009), Optimal Power System Generation Scheduling by Multi-Objective Genetic Algorithms with Preferences, Reliability Engineering and System Safety, 94: 432-444.   DOI
22 Zio, E., Bazzo, R. (2009), Multiobjective Reliability Allocation Problems by Fuzzy Preference Assignment on Level Diagrams