Multiobjective analysis

Sophos, A., Rotstein, E. and Stephanopoulos, G. (1980) Multiobjective analysis in modeling the petrochemical industry. Chemical Engineering Science,... 

In this study, operational risk was accounted for in terms of variance in both projected benefits, represented by first stage variables, and forecasted demand, represented by the recourse variables. The variability in the projected benefit represents the solution robustness where the model solution will remain close to optimal for all scenarios. On the other hand, variability of the recourse term represents the model robustness where the model solution will almost be feasible for all scenarios. This approach gives rise to a multiobjective analysis in which scaling factors are used to evaluate the sensitivity due to variations in each term. The projected benefits variation was scaled by 0i, and deviation from forecasted demand was scaled by 02, where different values of 0i and 02 were used in order to observe the sensitivity of each term on the final petrochemical complex. The objective function with risk consideration can be written as follows ... 

Current J, Min H, Schilling D (1990) Multiobjective analysis of facility location decisions. European Journal of Operational Research 49 295-307... 

Multiobjective Analysis for Energy and Resource Conservation in an Evaporation System... 

Nishitani, H. and Kunugita, E., "Multiobjective Analysis of Energy and Resource Conservation in an Evaporator System,"... 

Haimes, Y. Y., Tarvainen, K., Shima, T. and ThadathU, J. (1990). Hierarchical Multiobjective Analysis of Large-Scale Systems, Hemisphere Publishing, New York. 

Yamashita, F., Hara, H., Ito, T., Hashida, M. Novel hierarchical classification and visualization method for multiobjective optimization of drug properties application to structure-activity relationship analysis of cytochrome P450 metabolism. J. Chem. Inf. Model. 2008, 48, 364-9. 

Haimes, Y.Y. Hall, W.A., "Multiobjectives in Water Resources Systems Analysis The Surrogate Worth Trade-Off Method" Water Resources Research 1974, 10, 615. 

Optimization of WWTP control by means of multiobjective genetic algorithms and sensitivity analysis... 

Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M. and da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers an analysis and review, IEEE Transactions on Evolutionary Computation 7(2), pp. 117-132. 

Hakanen, J., Miettinen, K., Makela, M. and Manninen, J. (2005). On interactive multiobjective optimization with NIMBUS in chemical process design. Journal of Multicriteria Decision Analysis 13, pp. 125-134. 

Lim, Y., Floquet, R, Joulia, X. and Kim, S. (1999). Multiobjective optimization in terms of economics and potential environment impact for process design and analysis in a chemical process simulator, Industrial Engineering Chemistry Research 38, pp. 4729-4741. 

Haimes, Y. Y., and Hedl, W. A. (1974), Multiobjectives in Water Resources System Analysis The Surrogate Worth Trade off Method, Water Resources Research, Vol. 10, pp. 615-623. 

As discussed in Section 2, the system (flow control valve) under analysis might go through n states, before reaching the most critical deterioration state -I-1. In this paper, it is used the multiobjective GA integrated... 

Answer. Improved residue curve mapping technique, multilevel modeling approach, dynamic optimization of spatial and control structures, steady-state and dynamic behavior analysis, generic lumped reactive distillation volume element, multiobjective optimization criteria. 

You F, Tao L, Graziano DJ, Snyder SW. Optimal design of sustainable cellulosic biofuel supply chains multiobjective optimization coupled with hfe cycle assessment and input—output analysis. AIChE J 2012 58(4) 1157-80. 

Conversely, this is also one of the weaknesses of the software. As PROSUITE has its own economic and social databases, it only differentiates between European countries and other countries in the world. Therefore the users should expect that results are purely indicative and it could be site specific. Also it is designed to be used in European countries therefore the sustainabihty is assessed in creating a welfare, equity, and healthy environment for European communities (domestic) rather than for the place where the technology/product is appHed. Meanwhile, pathways to sustainabihty vary among regions and countries, and are subjectively dependent on the definition of win and the measure of success. It is expected that further research on multicriteria decision analysis/ multiobjective decision analysis would be conducted to improve the relevance and utihty of the results. 

Goicoechea, A., D. R. Hansen, and L. Duckstein.1982. Multiobjective Decision Analysis with Engineering and Business Applications. Chapter 9. New York Wiley. 

M. Anastasio, M. Kupinski, and R. Nishikawa . Optimization and FROC analysis of rule-based detection schemes using a multiobjective approach. IEEE Tmnsactions on Medical Imaging, 17 1089-1093,1998. 

E. Zitzler, L. Thiele, M. Laumanns, C.M. Fonseca, and V. Grunert da Fonseca. Performance Assessment of Multiobjective Optimizers An Analysis and Review. IEEE Transactions on Evolutionary Computation, 7(2) 117-132, 2003. 

In the last twenty years, various non-deterministic methods have been developed to deal with optimum design under environmental uncertainties. These methods can be classified into two main branches, namely reliability-based methods and robust-based methods. The reliability methods, based on the known probabiUty distribution of the random parameters, estimate the probability distribution of the system s response, and are predominantly used for risk analysis by computing the probability of system failure. However, variation is not minimized in reliability approaches (Siddall, 1984) because they concentrate on rare events at the tail of the probability distribution (Doltsinis and Kang, 2004). The robust design methods are commonly based on multiobjective minimization problems. The are commonly indicated as Multiple Objective Robust Optimization (MORO) and find a set of optimal solutions that optimise a performance index in terms of mean value and, at the same time, minimize its resulting dispersion due to input parameters uncertainty. The final solution is less sensitive to the parameters variation but eventually maintains feasibility with regards probabilistic constraints. This is achieved by the optimization of the design vector in order to make the performance minimally sensitive to the various causes of variation. 

In the following, first we describe oiu experimental setup (datasets and multiobjective algorithms), then we analyze the numerical results obtained in the different experiments with the approach described in the previous section. We finally report a brief analysis of the execution times of oiu experiments. 

Paravelli L (1989) Response-surface approach for reliability analysis. J Eng Mech 115 2763-2781 Fujimura K, Kiureghian AD (2007) Tail-equivalent linearization method for nonlinear random vibration. Probabilist Eng Mech 22 63-76 Furuta H, Kameda T (2006) Application of multiobjective genetic algorithm to bridge maintenance. In Ceragioli F, Dontchev A, Futura H, Marti K, Pandolfi L (eds) System modeling and optimization. 

---