We present a new method to aid a decision maker (DM) in selecting the “most preferred” from a set of design alternatives. The method is deterministic and assumes that the DM’s preferences reflect an implicit value function that is quasi-concave. The method is interactive, with the DM stating preferences in the form of attribute tradeoffs at a series of trial designs, each a specific design under consideration. The method is iterative and uses the gradient of the value function obtained from the preferences of the DM to eliminate lower value designs at each trial design. We present an approach for finding a new trial design at each iteration. We provide an example, the design selection for a cordless electric drill, to demonstrate the method. We provide results showing that (within the limit of our experimentation) our method needs only a few iterations to find the most preferred design alternative. Finally we extend our deterministic selection method to account for uncertainty in the attributes when the probability distributions governing the uncertainty are known.

1.
Fishburn, P. C., 1970, Utility Theory for Decision Making, John Wiley & Sons, New York.
2.
Yu, P. L., 1985, Multiple Criteria Decision Making: Concepts, Techniques and Extensions, Plenum Press, New York.
3.
Keeney, R. A., and Raiffa, H., 1976, Decision with Multiple Objectives Preferences and Value Tradeoffs, John Wiley & Sons, New York.
4.
Olson, D. L., 1996, Decision Aids for Selection Problems, Springer, New York.
5.
Zeleny, M., 1982, Multiple Criteria Decision Making, McGraw-Hill, New York.
6.
Marston, M., and Mistree, F., 1998, “An Implementation of Expected Utility Theory in Decision Based Design,” CD-ROM Proceedings of the ASME IDETC, Atlanta, GA.
7.
Lootsma, F. A., 1999, Multi-Criteria Decision Analysis via Ratio and Difference Judgment, Kluwer Academic, Norwell, MA.
8.
Saaty, T., 1980, Analytical Hierarchical Process, McGraw-Hill, New York.
9.
Thurston
,
D. L.
,
Carnahan
,
J. V.
, and
Liu
,
T.
,
1994
, “
Optimization of Design Utility
,”
ASME J. Mech. Des.
,
116
, pp.
801
808
.
10.
Thurston
,
D. L.
,
2001
, “
Real and Misconceived Limitations to Decision Based Design With Utility Analysis
,”
ASME J. Mech. Des.
,
123
, pp.
176
182
.
11.
Korhonen
,
P.
,
Wallenius
,
J.
, and
Zionts
,
S.
,
1984
, “
Solving the Discrete Multiple Criteria Problem Using Convex Cones
,”
Manage. Sci.
,
30
, pp.
1336
1345
.
12.
Malakooti
,
B.
,
1988
, “
A Decision Support System and A Heuristic Interactive Approach for Solving Discrete Multiple Criteria Problems
,”
IEEE Trans. Syst. Man Cybern.
,
18
, pp.
273
284
.
13.
Koksalan
,
M.
,
Karwan
,
M. H.
, and
Zionts
,
S.
,
1984
, “
An Improved Method for Solving Multiple Criteria Problems Involving Discrete Alternatives
,”
IEEE Trans. Syst. Man Cybern.
,
14
, pp.
24
34
.
14.
Karwan
,
M. H.
,
Ramesh
,
R.
, and
Zionts
,
S.
,
1989
, “
Preference Structure Representation Using Convex Cones in Multi-Criteria Integer Programming
,”
Manage. Sci.
,
35
, pp.
1092
1105
.
15.
Malakooti
,
B.
,
1989
, “
Theories and An Exact Interactive Paired-Comparison Approach for Discrete Multiple-Criteria Problems
,”
IEEE Trans. Syst. Man Cybern.
,
19
, pp.
365
378
.
16.
Barzilai, J., 1997, “On Linear Value Functions and Hierarchical Decomposition,” Proceedings of International Conference on Methods and Applications of Multi-Criteria Decision Making, pp. 315–318.
17.
Geoffrion
,
A. M.
,
Dyer
,
J. S.
, and
Feinberg
,
A.
,
1972
, “
An Interactive Approach for Multi-Criterion Optimization With an Application to the Operation of an Academic Department
,”
Manage. Sci.
,
19
, pp.
357
368
.
18.
Musselman
,
K.
, and
Talavage
,
J.
,
1980
, “
A Tradeoff Cut Approach to Multiple Objective Optimization
,”
Oper. Res.
,
28
, pp.
1424
1435
.
19.
Bazaraa, M. S., Sherali, H. D., and Shetty, C. M., 1993, Nonlinear Programming: Theory and Algorithms, John Wiley & Sons, New York.
20.
Mangasarian, O. L., 1969, Nonlinear Programming, McGraw-Hill, New York.
21.
Sundaram, R. K., 1996, First Course in Optimization Theory, Cambridge University Press, U.K.
22.
Barzilai
,
J.
,
1998
, “
On the Decomposition of Value Functions
,”
Oper. Res. Lett.
,
22
, pp.
159
170
.
23.
Takayama, A., 1993, Analytical Methods in Economics, The University of Michigan Press, Ann Arbor, MI.
24.
Hazelrigg
,
G. A.
,
1998
, “
A Framework for Decision-Based Design
,”
ASME J. Mech. Des.
,
120
, pp.
653
658
.
25.
Bradley
,
S. R.
, and
Agogino
,
A. M.
,
1994
, “
An Intelligent Real Time Design Methodology for Component Selection: An Approach to Managing Uncertainty
,”
ASME J. Mech. Des.
,
116
, pp.
980
988
.
26.
Li
,
H.
, and
Azarm
,
S.
,
2000
, “
Product Design Selection Under Uncertainty and With Competitive Advantage
,”
ASME J. Mech. Des.
,
122
, pp.
411
418
.
27.
Wan
,
J.
, and
Krishnamurty
,
S.
,
2001
, “
Learning-Based Preference Modeling in Engineering Design Decision-Making
,”
ASME J. Mech. Des.
,
123
, pp.
191
198
.
28.
Li
,
H.
, and
Azarm
,
S.
,
2002
, “
An Approach for Product Line Design Selection Under Uncertainty and Competition
,”
ASME J. Mech. Des.
,
124
, pp.
385
392
.
29.
Wassenaar
,
H. J.
, and
Chen
,
W.
,
2003
, “
An Approach to Decision-Based Design With Discrete Choice Analysis for Demand Modeling
,”
ASME J. Mech. Des.
,
125
, pp.
490
497
.
30.
Maddulapalli, K., Azarm, S., and Boyars, A., 2002, “Interactive Product Design Selection With an Implicit Value Function,” CD-ROM Proceedings of the ASME IDETC, Montreal, Canada.
You do not currently have access to this content.