Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control
We present a new two-step approach for automatized a posteriori decision making in multi-objective optimization problems, i.e., selecting a solution from the Pareto front. In the first step, a knee region is determined based on the normalized Euclidean distance from a hyperplane defined by the furth...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Inventions |
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| Online Access: | https://www.mdpi.com/2411-5134/7/3/46 |
| _version_ | 1797486755097608192 |
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| author | Thomas Schmitt Matthias Hoffmann Tobias Rodemann Jürgen Adamy |
| author_facet | Thomas Schmitt Matthias Hoffmann Tobias Rodemann Jürgen Adamy |
| author_sort | Thomas Schmitt |
| collection | DOAJ |
| description | We present a new two-step approach for automatized a posteriori decision making in multi-objective optimization problems, i.e., selecting a solution from the Pareto front. 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 solution 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. The proposed approach is applied in simulation for the multi-objective model predictive control (MPC) of the two-dimensional rocket car example and the energy management system of a building. |
| first_indexed | 2024-03-09T23:37:47Z |
| format | Article |
| id | doaj.art-38beb01491734391a3e073e82b1252d4 |
| institution | Directory Open Access Journal |
| issn | 2411-5134 |
| language | English |
| last_indexed | 2024-03-09T23:37:47Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Inventions |
| spelling | doaj.art-38beb01491734391a3e073e82b1252d42023-11-23T16:56:15ZengMDPI AGInventions2411-51342022-06-01734610.3390/inventions7030046Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive ControlThomas Schmitt0Matthias Hoffmann1Tobias Rodemann2Jürgen Adamy3Honda Research Institute Europe GmbH, 63073 Offenbach, GermanySystems Modeling and Simulation, Systems Engineering, Saarland University, 66123 Saarbrücken, GermanyHonda Research Institute Europe GmbH, 63073 Offenbach, GermanyControl Methods & Robotics Lab, Technical University of Darmstadt, 64283 Darmstadt, GermanyWe present a new two-step approach for automatized a posteriori decision making in multi-objective optimization problems, i.e., selecting a solution from the Pareto front. 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 solution 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. The proposed approach is applied in simulation for the multi-objective model predictive control (MPC) of the two-dimensional rocket car example and the energy management system of a building.https://www.mdpi.com/2411-5134/7/3/46energy management system (EMS)MPCnormal boundary intersection (NBI)Pareto optimizationknee regionPARODIS |
| spellingShingle | Thomas Schmitt Matthias Hoffmann Tobias Rodemann Jürgen Adamy Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control Inventions energy management system (EMS) MPC normal boundary intersection (NBI) Pareto optimization knee region PARODIS |
| title | Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control |
| title_full | Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control |
| title_fullStr | Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control |
| title_full_unstemmed | Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control |
| title_short | Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control |
| title_sort | incorporating human preferences in decision making for dynamic multi objective optimization in model predictive control |
| topic | energy management system (EMS) MPC normal boundary intersection (NBI) Pareto optimization knee region PARODIS |
| url | https://www.mdpi.com/2411-5134/7/3/46 |
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