An information-passing strategy for achieving Pareto optimality in the design of complex systems
As engineering systems grow in complexity, it becomes more challenging to achieve system-level designs that effectively balance the trade-offs among subsystems. Lewis and others have developed a well-known, traditional game-theoretic approach for formally modeling complex systems that can locate a N...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2012
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| In: |
Research in engineering design
Year: 2011, Jahrgang: 23, Heft: 1, Pages: 71-83 |
| ISSN: | 1435-6066 |
| DOI: | 10.1007/s00163-011-0115-8 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1007/s00163-011-0115-8 Verlag, Volltext: https://link.springer.com/article/10.1007/s00163-011-0115-8 
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| Verfasserangaben: | Francesco Ciucci, Tomonori Honda, Maria C. Yang |
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| 520 | |a As engineering systems grow in complexity, it becomes more challenging to achieve system-level designs that effectively balance the trade-offs among subsystems. Lewis and others have developed a well-known, traditional game-theoretic approach for formally modeling complex systems that can locate a Nash equilibrium design with a minimum of information sharing in the form of a point design. This paper builds on Lewis’ work by proposing algorithms that are capable of converging to Pareto-optimal system-level designs by increasing cooperation among subsystems through additional passed information. This paper investigates several forms for this additional passed information, including both quadratic and eigen-based formulations. Such forms offer guidance to designers on how they should change parameter values to better suit the overall system by providing information on directionality and curvature. Strategies for representing passed information are examined in three case studies of 2- and 3-player scenarios that cover a range of system complexity. Depending on the scenario, findings suggest that passing more information generally leads to convergence to a Pareto-optimal set. However, more iterations may be required to reach the Pareto set than if using a traditional game-theoretic approach. | ||
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