Representing and Aggregating Engineering Quantities with Preference Structure for Set-based Concurrent Engineering

Abstract

The early phase of design intrinsically contains multiple sources of uncertainties in describing the design, and nevertheless the decision-making process at this phase exerts a critical effect upon drawing a successful design. Recently, set-based concurrent engineering (SBCE) has been attracting public attention as an emerging CE paradigm. Representing and communicating set-valued information are useful in handling engineering uncertainties and efficient for the collaboration between designers in the CE environment. Even if we can readily know that SBCE is efficient and powerful enough for large-scale design and management, the existing literature does not give the details of methods to achieve SBCE principles. We have developed a computer-based framework, called ‘preference set-based design’ (PSD), for implementing SBCE principles, which consists of set representation, propagation, modification, narrowing, and aggregation methods. This article focuses on the set representation and aggregation methods that play an important role in the CE environment. This article proposes new engineering quantities that can capture the designer’s preference structure on both the continuous and the discrete sets. A new aggregation method is presented to aggregate the proposed engineering quantities. A comparative analysis with the existing representation and aggregation techniques is also provided.

Keywords

set-based concurrent engineering set representation set aggregation preference measure of uncertainty

Get full access to this article

View all access options for this article.

References

Nahm, Y.-E. and Ishikawa, H. (2004). Integrated Product and Process Modeling for Collaborative Design Environment , Concurrent Engineering: Research and Applications, 12 (1): 5-23 .

Nahm, Y.-E. and Ishikawa, H. (2005). A Hybrid Multi-Agent System Architecture for Enterprise Integration Using Computer Networks , Robotics and Computer-Integrated Manufacturing, 21 (3): 217-234 .

Danesh, M.R. and Jin, Y. (2001). An Agent-Based Decision Network for Concurrent Engineering Design , Concurrent Engineering: Research and Applications, 9 (1): 37-47 .

Ishikawa, H. and Nahm, Y.-E. (2003). Set-Based Design Support by Using Multi-Agent Approach , In: Proceedings of the 10th ISPE International Conference on Concurrent Engineering: Research and Applications, Book 2, Maderia, Portugal, July 26-30, pp. 117-122 .

Finch, W.W. (1997). Predicate Logic Representations for Design Constraints on Uncertainty Supporting the Set-Based Design Paradigm, Doctoral Dissertation, Department of Mechanical Engineering, The University of Michigan.

Wood, K.L. and Antonsson, E.K. (1989). Computations with Imprecise Parameters in Engineering Design: Background and Theory , ASME Journal of Mechanisms, Transmissions, and Automation in Design, 111 (4): 616-625 .

Wallace, D.R. , Jakiela, M.J. and Flowers, W.C. (1996). Design Search under Probabilistic Specifications Using Genetic Algorithms , Computer-Aided Design, 28 (5): 405-421 .

Chen, W. and Yuan, C. (1999). A Probabilistic-Based Design Model for Achieving Flexibility in Design , ASME Journal of Mechanical Design, 121 (1): 77-83 .

Sobek II, D.K. , Ward, A.C. and Liker, J.K. (1999). Toyota’s Principles of Set-Based Concurrent Engineering , Sloan Management Review, 40 (2): 67-83 .

10.

Lu, S.C.Y. , Bukkapatnam, S.T.S. , Ge, P. and Wang, N. (1999). Backward Mapping Methodology for Design Synthesis , In: Proceedings of 1999 ASME Design Engineering Technical Conference, DETC99/DTM-8766, Lag Vegas, NV.

11.

Liker, J.K. , Sobek II, D.K. , Ward, A.C. and Cristiano, J.J. (1996). Involving Suppliers in Product Development in the United States and Japan: Evidence for Set-Based Concurrent Engineering , IEEE Transactions on Engineering Management, 43 (2): 165-178 .

12.

Terwiesch, C. , de Meyer, A. and Loch, C.H. (2002). Exchanging Preliminary Information in Concurrent Engineering: Alternative Coordination Strategies , Organization Science, 13 (4): 402-419 .

13.

Ward, A.C. , Liker, J.K. , Cristiano, J.J. and Sobek II, D.K. (1995). The Second Toyota Paradox: How Delaying Decisions Can Make Better Cars Faster , Sloan Management Review, 36 (3): 43-61 .

14.

Nahm, Y.-E. and Ishikawa, H. (2005). Novel Space-Based Design Methodology for Preliminary Engineering Design , International Journal of Advanced Manufacturing Technology (in press).

15.

Nahm, Y.-E. and Ishikawa, H. (2005). A Flexible and Robust Approach for Preliminary Engineering Design Based on Designer’s Preference , Computer-Aided Design (to appear).

16.

Ward, A.C. , Lozano-Perez, T. and Seering, W.P. (1990). Extending the Constraint Propagation of Intervals, Artificial Intelligence for Engineering Design , Analysis and Manufacturing, 4 (1): 47-54 .

17.

Finch, W.W. and Ward, A.C. (1995). Generalized Set-Propagation Operations over Relations of More than Three Variables, Artificial Intelligence for Engineering Design , Analysis and Manufacturing, 9: 231-242 .

18.

Scott, M.J. and Antonsson, E.K. (1998). Aggregation Functions for Engineering Design Trade-offs , Fuzzy Sets and Systems, 99 (3): 253-264 .

19.

Parunak, H.V.D. , Ward, A.C. and Sauter, J. (1999). The MarCon Algorithm: A Systematic Market Approach to Distributed Constraint Problems, Artificial Intelligence for Engineering Design , Analysis and Manufacturing, 13 (3): 217-234 .

20.

Agarwal, H. , Renaud, J.E. , Preston, E.L. and Padmanabham, D. (2004). Uncertainty Quantification Using Evidence Theory in Multidisciplinary Design Optimization , Reliability Engineering and System Safety, 85 (1-3): 281-294 .

21.

Sahinidis, N.V. (2004). Optimization Under Uncertainty: State-of-the-art and Opportunities , Computers and Chemical Engineering, 28 (6-7): 971-983 .

22.

Lial, M.L. , Greenwell, R.N. and Ritchey, N.P. (2002). Finite Mathematics, Addison Welsey .

23.

Otto, K.N. and Antonsson, E.K. (1991). Trade-off Strategies in Engineering Design , Research in Engineering Design, 3 (2): 87-104 .

24.

Scott, M.J. and Antonsson, E.K. (2000). Using Indifferent Points in Engineering Decisions , In: Proceedings of 2000 ASME Design Engineering Technical Conference, DETC2000/DTM-14559, Baltimore, MD, September 10-13.

25.

Ward, A.C. (1990). A Recursive Model for Managing the Design Process , In: Proceedings of 1990 ASME Conference on Design Theory and Methodology, Chicago, IL, pp. 47-52 .

26.

Kusiak, A. and Wang, J. (1994). Negotiation in Engineering Design , Group Decision and Negotiation, 3: 69-91 .

27.

Zimmermann, H.-J. (2001). Fuzzy Set Theory and its Applications, Kluwer Academic Publishers .

28.

Luoh, L. and Wang, W.-J. (2000). A Modified Entropy for General Fuzzy Sets , International Journal of Fuzzy Systems, 2 (4): 300-304 .

29.

Wood, K.L. , Antonsson, E.K. and Beck, J.L. (1989). Comparing Fuzzy and Probability Calculus for Representing Imprecision in Preliminary Engineering Design , In: Proceedings of the First International ASME Design Theory and Methodology Conference, Montreal, Canada, September 18-19, pp. 99-105 .

30.

Kirschman, C.F. and Fadel, G.M. (1997). Customer Metrics for the Selection of Generic Forms at the Conceptual Stage of Mechanical Design , In: Proceedings of 1997 ASME Design Engineering Technical Conference, DETC97/DTM-3877, Sacramento, CA, September 14-17.