Changing Subsystem Information Strategies Using Weighted Objectives: Increasing Robustness to Biased Information Passing

Complex system design requires managing competing objectives between many subsystems. Previous field research has demonstrated that subsystem designers may use biased information passing as a negotiation tactic and thereby reach sub-optimal system-level results due to local optimization behavior. One strategy to combat the focus on local optimization is an incentive structure that promotes system-level optimization. This paper presents a new subsystem incentive structure based on Multi-disciplinary Optimization (MDO) techniques for improving robustness of the design process to such biased information passing strategies. Results from simulations of different utility functions for a test suite of multi-objective problems quantify the system robustness to biased information passing strategies. Results show that incentivizing subsystems with this new weighted structure may decrease the error resulting from biased information passing.Copyright © 2015 by ASME

[1]  Kari Sentz,et al.  Probabilistic bounding analysis in the Quantification of Margins and Uncertainties , 2011, Reliab. Eng. Syst. Saf..

[2]  Herbert A. Simon,et al.  The Structure of Ill Structured Problems , 1973, Artif. Intell..

[3]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[4]  Michael L. Tushman,et al.  Competing by design: the power of organizational architecture , 1998 .

[5]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[6]  Kemper Lewis,et al.  A comprehensive robust design approach for decision trade-offs in complex systems design , 2001 .

[7]  Mark Klein,et al.  The Dynamics of Collaborative Design: Insights from Complex Systems and Negotiation Research , 2003, Concurr. Eng. Res. Appl..

[8]  Alex H. B. Duffy,et al.  Distributed design coordination , 2002 .

[9]  Weiming Shen,et al.  Collaborative conceptual design - state of the art and future trends , 2002, Comput. Aided Des..

[10]  John O. Ledyard,et al.  A Market-Based Mechanism for Allocating Space Shuttle Secondary Payload Priority , 2000 .

[11]  Deborah G. Ancona,et al.  Managing for the Future: Organizational Behavior and Processes , 1998 .

[12]  Marshall B. Jones,et al.  Isoperformance: Analysis and design of complex systems with desired outcomes , 2006, Syst. Eng..

[13]  M E Paté-Cornell,et al.  Organizational aspects of engineering system safety: the case of offshore platforms. , 1990, Science.

[14]  Kemper Lewis An Algorithm for Integrated Subsystem Embodiment and System Synthesis , 2013 .

[15]  Hiromu Ohno,et al.  Optimal Design of a Large Complex System from the Viewpoint of Sensitivity Analysis , 1970 .

[16]  Tomasz Arciszewski,et al.  Emergent Designer: An Integrated Research and Design Support Tool Based on Models of Complex Systems , 2005, J. Inf. Technol. Constr..

[17]  Kemper Lewis,et al.  A study of convergence in decentralized design processes , 2005 .

[18]  John E. Taylor,et al.  Emergence and Role of Cultural Boundary Spanners in Global Engineering Project Networks , 2010 .

[19]  Kemper Lewis,et al.  Using Bounded Rationality to Improve Decentralized Design , 2008 .

[20]  I. Y. Kim,et al.  Adaptive weighted-sum method for bi-objective optimization: Pareto front generation , 2005 .

[21]  Gyung-Jin Park,et al.  Comparison of MDO methods with mathematical examples , 2008 .

[22]  Kemper Lewis,et al.  Equilibrium stability in decentralized design systems , 2005, Int. J. Syst. Sci..

[23]  Kemper Lewis,et al.  A Comparison of Information Passing Strategies in System Level Modeling , 2010 .

[24]  Wei Chen,et al.  Decision Making in Engineering Design , 2006 .

[25]  Claudia Eckert,et al.  Design margins as a key to understanding design iteration , 2014 .

[26]  Eric van Damme,et al.  Non-Cooperative Games , 2000 .

[27]  John E. Renaud,et al.  Worst case propagated uncertainty of multidisciplinary systems in robust design optimization , 2000 .

[28]  Jon C. Helton,et al.  Quantification of margins and uncertainties: Conceptual and computational basis , 2011, Reliab. Eng. Syst. Saf..

[29]  Farrokh Mistree,et al.  Collaborative multidisciplinary decision making using game theory and design capability indices , 2005 .

[30]  Maria C. Yang,et al.  An information-passing strategy for achieving Pareto optimality in the design of complex systems , 2011, Research in Engineering Design.

[31]  Fumihiko Kimura,et al.  The Virtual Maintenance System: A Computer-based Support Tool for Robust Design , 2000 .

[32]  Daniel P. Thunnissen,et al.  Method for Determining Margins in Conceptual Design , 2004 .

[33]  Kemper Lewis,et al.  Modeling Interactions in Multidisciplinary Design: A Game Theoretic Approach , 1997 .

[34]  Paul Collopy,et al.  ECONOMIC-BASED DISTRIBUTED OPTIMAL DESIGN , 2001 .

[35]  Robert P. Smith,et al.  Identifying Controlling Features of Engineering Design Iteration , 2015 .

[36]  Lieve Weytjens,et al.  Design Support Tools in Practice. The Architects' Perspective , 2011 .

[37]  Jaroslaw Sobieszczanski-Sobieski,et al.  Multidisciplinary aerospace design optimization - Survey of recent developments , 1996 .

[38]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007, DAC 2006.

[39]  Joaquim R. R. A. Martins,et al.  Multidisciplinary design optimization: A survey of architectures , 2013 .

[40]  John R. Olds,et al.  Evaluation of Multidisciplinary Optimization Techniques Applied to a Reusable Launch Vehicle , 2006 .

[41]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[42]  Ali Yassine,et al.  Complex Concurrent Engineering and the Design Structure Matrix Method , 2003, Concurr. Eng. Res. Appl..

[43]  Matthias Jarke,et al.  The brave new world of design requirements , 2011, Inf. Syst..