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Research Papers: Design Theory and Methodology

Biased Information Passing Between Subsystems Over Time in Complex System Design

[+] Author and Article Information
Jesse Austin-Breneman

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jausbren@umich.edu

Bo Yang Yu

Department of Mechanical Engineering,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: byyu@mit.edu

Maria C. Yang

Department of Mechanical Engineering,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: mcyang@mit.edu

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 27, 2015; final manuscript received September 28, 2015; published online November 4, 2015. Assoc. Editor: Kristina Shea.

J. Mech. Des 138(1), 011101 (Nov 04, 2015) (9 pages) Paper No: MD-15-1171; doi: 10.1115/1.4031745 History: Received February 27, 2015; Revised September 28, 2015

During the early stage design of large-scale engineering systems, design teams are challenged to balance a complex set of considerations. The established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice suboptimal system-level results are often reached due to factors such as satisficing, ill-defined problems, or other project constraints. Twelve subsystem and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate subsystems in their own work. Responses showed subsystem team members often presented conservative, worst-case scenarios to other subsystems when negotiating a tradeoff as a way of hedging against their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled in this paper with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias, and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

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Figures

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Fig. 1

System schematic for one iteration

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Fig. 2

Normalized distance to the Pareto frontier for all three test conditions

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Fig. 3

Average number of system iterations for all three test conditions

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Fig. 4

Solution path in the no bias condition b = 0

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Fig. 5

Solution path in the static bias condition b = 1.3

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Fig. 6

Solution path in the decreasing bias condition b=1.3−.1*i

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Fig. 10

Number of iterations for asymmetric decreasing bias conditions

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Fig. 7

Normalized distance to Pareto frontier for asymmetric constant bias conditions

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Fig. 8

Normalized distance to Pareto frontier for asymmetric decreasing bias conditions

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Fig. 9

Number of iterations for asymmetric constant bias conditions

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