Abstract
Model predictive (or sequential approximate) optimization methods find an optimal solution in parallel with predicting the function forms in mathematical models when those forms are not known explicitly in terms of design variables. In this paper, under a dynamic environment with multiple objectives, we propose a model predictive optimization method using computational intelligence in particular support vector regression and the satisficing trade-off method. The effectiveness of the proposed method will be shown along a numerical example.
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Notes
- 1.
For a given parameter \(\epsilon > 0,\ {L}^{\epsilon }(z,y,f) = \vert y - f(z){\vert }_{\epsilon } =\max (0,\vert y - f(z)\vert - \epsilon )\).
- 2.
A decision maker may change her/his aspiration level from the one at the previous time t − 1.
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Nakayama, H., Yun, Y., Shirakawa, M. (2010). Multi-objective Model Predictive Control. In: Ehrgott, M., Naujoks, B., Stewart, T., Wallenius, J. (eds) Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems. Lecture Notes in Economics and Mathematical Systems, vol 634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04045-0_24
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DOI: https://doi.org/10.1007/978-3-642-04045-0_24
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