Research Papers

An Entropic Method for Sequencing Discrete Design Decisions

[+] Author and Article Information
Chiradeep Sen

Department of Mechanical Engineering, Clemson University, Clemson, SC 29634-0921csen@clemson.edu

Farhad Ameri

Department of Engineering Technology, Texas State University, San Marcos, TX 78666ameri@txstate.edu

Joshua D. Summers

Department of Mechanical Engineering, Clemson University, Clemson, SC 29634-0921jsummer@clemson.edu

J. Mech. Des 132(10), 101004 (Sep 30, 2010) (11 pages) doi:10.1115/1.4002387 History: Received November 08, 2009; Revised August 02, 2010; Published September 30, 2010; Online September 30, 2010

This paper presents a mathematical model for quantifying uncertainty of a discrete design solution and to monitor it through the design process. In the presented entropic view, uncertainty is highest at the beginning of the process as little information is known about the solution. As additional information is acquired or generated, the solution becomes increasingly well-defined and uncertainty reduces, finally diminishing to zero at the end of the process when the design is fully defined. In previous research, three components of design complexity—size, coupling, and solvability—were identified. In this research, these metrics are used to model solution uncertainty based on the search spaces of the variables (size) and the compatibility between variable values (coupling). Solvability of the variables is assumed uniform for simplicity. Design decisions are modeled as choosing a value, or a reduced set of values, from the existing search space of a variable, thus, reducing its uncertainty. Coupling is measured as the reduction of a variable’s search space as an effect of reducing the search space of another variable. This model is then used to monitor uncertainty reduction through a design process, leading to three strategies that prescribe deciding the variables in the order of their uncertainty, number of dependents, or the influence of on other variables. Comparison between these strategies shows how size and coupling of variables in a design can be used to determine task sequencing strategy for fast design convergence.

Copyright © 2010 by American Society of Mechanical Engineers
Topics: Design , Space , Project tasks
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Figure 3

Cij as a linear function of S(Xj∣Xi)

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Figure 4

Two configurations of dependencies based on selection of process sequence

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Figure 5

Modified discrete design graph for two variables, showing the reflexive and symmetric nature of dependencies

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Figure 6

Discrete design graph for the skate wheel problem

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Figure 2

A simple discrete design graph with two variables and one dependency

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Figure 1

Example discrete design problem: nonpneumatic wheel for inline skates

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Figure 7

Uncertainty reduction in scenario-1

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Figure 8

Uncertainty reduction in scenario-2

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Figure 9

Uncertainty reduction in scenario-3

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Figure 10

Uncertainty reduction in scenario-4




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