Introducing Multiobjective Complex Systems

Tobias Dietz; Kathrin Klamroth; Konstantin Kraus; Stefan Ruzika; Luca E. Schäfer; Britta Schulze; Michael Stiglmayr; Margaret M. Wiecek

arXiv · 1811.06431

Introducing Multiobjective Complex Systems

Tobias Dietz·Kathrin Klamroth·Konstantin Kraus·Stefan Ruzika·Luca E. Schäfer·Britta Schulze·Michael Stiglmayr·Margaret M. Wiecek

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Abstract

This article focuses on the optimization of a complex system which is composed of several subsystems. On the one hand, these subsystems are subject to multiple objectives, local constraints as well as local variables, and they are associated with an own, subsystem-dependent decision maker. On the other hand, these subsystems are interconnected to each other by global variables or linking constraints. Due to these interdependencies, it is in general not possible to simply optimize each subsystem individually to improve the performance of the overall system. This article introduces a formal graph-based representation of such complex systems and generalizes the classical notions of feasibility and optimality to match this complex situation. Moreover, several algorithmic approaches are suggested and analyzed.

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Tobias Dietz, Kathrin Klamroth, Konstantin Kraus, Stefan Ruzika, Luca E. Schäfer, Britta Schulze, Michael Stiglmayr, Margaret M. Wiecek. 2018-11-15. Introducing Multiobjective Complex Systems. https://arxiv.org/abs/1811.06431

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