An Approach to Improved Decentralized Design: The Modified Approximation-Based Decentralized Design (MADD) Framework

Abstract

The design of large scale complex engineering systems requires interaction and communication between multiple disciplines and decentralized subsystems. One common fundamental assumption in decentralized design is that the individual subsystems only exchange design variable information and do not share objective functions or gradients. This is because the decentralized subsystems can either not share this information due to geographical constraints or choose not to share it due to corporate secrecy issues. Game theory has been used to model the interactions between distributed design subsystems and predict convergence and equilibrium solutions. These game theoretic models assume that designers make perfectly rational decisions by selecting solutions from their Rational Reaction Set (RRS), resulting in a Nash Equilibrium solution. However, empirical studies reject the claim that decision makers always make rational choices and the concept of Bounded Rationality is used to explain such behavior. In this paper, a framework is proposed that uses the idea of bounded rationality in conjunction with set-based design, metamodeling and multiobjective optimization techniques to improve solutions for convergent decentralized design problems. Through the use of this framework, entitled Modified Approximation-based Decentralized Design (MADD) framework, convergent decentralized design problems converge to solutions that are superior to the Nash equilibrium. A two subsystem mathematical problem is used as case study and simulation techniques are used to study the impact of the framework parameters on the final solution. The discipline specific objective functions within the case study problem are unconstrained and continuous — however, the implementation of the MADD framework is not restricted to such problems.