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Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control

Thomas Schmitt, Matthias Hoffmann, Tobias Rodemann, Jürgen Adamy, "Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control", Inventions 2022, vol. 7, no. 3, 2022.

Abstract

We present a new two-step approach for automatized a posteriori decision making in multi-objective optimization problems. In the first step, a knee region is determined based on the normalized Euclidean distance from a hyperplane defined by the furthest Pareto solution and the negative unit vector. The size of the knee region depends on the Pareto front’s shape and a design parameter. In the second step, preferences for all objectives formulated by the decision maker, e. g. 50-20-30 for a 3D problem, are translated into a hyperplane which is then used to choose a final decision from the knee region. This way, the decision maker’s preference can be incorporated while its influence depends on the Pareto front’s shape and a design parameter, at the same time favorizing knee points if they exist. A simulation case study of an energy management system with multi-objective model predictive control (MPC) shows the applicability of the approach.



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