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Journal of Mechanical Design | Volume 129 | Issue 10 | Research Paper
RESEARCH PAPERS

A Method to Ensure Preference Consistency in Multi-Attribute Selection Decisions

[+] Author and Article Information
Michael Kulok

Department of Mechanical and Aerospace Engineering, University at Buffalo-SUNY, Buffalo, NY 14260

Kemper Lewis1

Department of Mechanical and Aerospace Engineering, University at Buffalo-SUNY, Buffalo, NY 14260kelewis@buffalo.edu

1

Corresponding author.

J. Mech. Des 129(10), 1002-1011 (Dec 18, 2006) (10 pages) doi:10.1115/1.2761921 History: Received July 20, 2006; Revised December 18, 2006
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A number of approaches for multi-attribute selection decisions exist, each with certain advantages and disadvantages. One method that has recently been developed, called the hypothetical equivalents and inequivalents method (HEIM) supports a decision maker (DM) by implicitly determining the importances a DM places on attributes using a series of simple preference statements. In this and other multi-attribute selection methods, establishing consistent preferences is critical in order for a DM to be confident in his/her decision and its validity. In this paper, a general preference consistency method is developed, which is used to ensure that a consistent preference structure exists for a given DM. The method is demonstrated as part of HEIM, but is generalizable to any cardinal or ordinal preference structure, where the preferences can be over alternatives or attributes. These structures play an important role in making selection decisions in engineering design including selecting design concepts, materials, manufacturing processes, and configurations, among others. The theoretical foundations of the method are developed and the need for consistent preferences is illustrated in the application to a drill selection case study where the decision maker expresses inconsistent preferences.
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Grahic Jump Location
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Feasible design space

Grahic Jump Location
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Feasible design space reduction

Grahic Jump Location
+Value function representations
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Value function representations
Figure 1

Value function representations

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