Abstract
In this chapter, we describe a hybrid decision model and a multiagent framework for collaborative decision support in the design process. The proposed knowledge-based collaborative decision support model can quantitatively incorporate qualitative design knowledge and preferences for multiple, conflicting attributes stored in a knowledge repository so that a better understanding of the consequences of design decisions can be achieved from an overall perspective. The multiagent framework provides an efficient decision support environment involving distributed resources to shorten the realization of products with optimal life-cycle performance and competitiveness. The developed model and framework are generic and flexible enough to be used in a variety of design decision problems. The framework is illustrated with an application in concept evaluation and selection in power-supply product family design for mass customization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dasgupta D, McGregor DR (1994) A more biologically motivated genetic algorithm: the model and some results. Cybernetics and Systems: An International Journal 25: 447–469
Forgionne GA (1994) Decision technology system to deliver effective concurrent engineering. Concurrent Engineering: Research and Applications 2(2): 67–76.
Gui JK (1993) Methodology for Modeling Complete Product Assemblies, PhD Dissertation, Helsinki University of Technology, Finland
Hazelrigg G (1996) System Engineering: An Approach to Information-based Design, Prentice-Hall International Series in Industrial and System Engineering
Jiao JX, Tseng MM (1998) Fuzzy ranking for concept evaluation in configuration design for mass customization. Concurrent Engineering: Research and Application 6(3):189–206
Keeney RL, Raiffa H (1993) Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Cambridge University Press
Kickert WJM (1978) Fuzzy Theories on Decision-making: A Critical Review, Martinus Nijhoff Social Sciences Division
Kim JS (2003) Negotiation support in electronic commerce using fuzzy membership functions and AHP, Proceedings of the 6th Pacific Rim International Workshop on Multi-Agents (PRIMA) 2003, Seoul (Korea), pp.93–104
Lu J, Quaddus MA, Williams R (2000) Developing a Knowledge-based Multi-Objective Decision Support System, Proceedings of the 33rd Hawaii International Conference on System Sciences, pp. 1–10
Lambright JP, Ume C (1996) A flat composite panel design advisory system using knowledge based and case based reasoning. Journal of Mechanical Design 118 December: 461–475.
Mistree F, Hughes OF, Bras BA (1993) The Compromise Decision Support Problem and the Adaptive Linear Programming Algorithm, Structural Optimization: Status and Promise, M.P. Kamatt (ed.), AIAA, Washington D. C., Chapter 11, pp 247–286
Mistree F, Bras B, Smith WF, Allen JK (1995) Modeling design processes: a conceptual decision-based perspective. Engineering Design & Automation, 1(4): 209–321
Pahl G, Beitz W (1996) Engineering Design-A Systematic Approach, New York: Springer
Rosen DW, Chen Y, Gerhard J, Allen JK, Mistree F (2000) Design Decision Templates and Their Implementation for Distributed Design and Solid Freeform Fabrication, Proceedings of ASME DETC00, Chapter No. DETC00/DAC-14293, Baltimore, Maryland, September 10–13
Sriram RD (1997) Intelligent Systems for Engineering: A Knowledge-based Approach. London: Springer Verlag, UK
Sriram RD (2002) Distributed and Integrated Collaborative Engineering Design. Sarven Publishers, Glenwood, MD, USA
Suh NP (1990) The Principles of Design. Oxford University Press, New York, NY
Saaty TL (1991) The Analytic Hierarchy Process. McGraw-Hill, New York, NY
Simon HA (1976) Administrative Behavior. Free Press, New York
Szykman S, Sriram RD, Regli W (2001) The role of knowledge in next-generation product development system. ASME Journal of Computing and Information Science in Engineering 1(1): 3–11
Talukdar S, Baerentzen L, Gove A, de Souza P (1996) Asynchronous Teams: Cooperation Schemes for Autonomous Agents. Carnegie Mellon University, Pittsburgh, PA 15213
Xiao A, Choi H, Allen JK, Rosen DW, Mistree F (2002) Collaborative Decision-making Across Digital Interfaces, Proceedings of ASME DETC 2002, Paper No.: DETC2002/DAC-34073, Montreal, Canada
Zadeh LA (1965) Fuzzy sets. Information and Control 8: 338–353
Zha XF, (2003) Knowledge intensive decision support for design process: a hybrid robust model and framework. Proceedings of ICED 03, Stockholm, Sweden
Zha XF, Sriram RD, Lu WF (2003) Knowledge Intensive Collaborative Decision Support for Design Process. Proceedings of ASME DETC 2003, Paper No: DETC2003/DAC-48747, Chicago
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag London Limited
About this chapter
Cite this chapter
Zha, X.F., Sriram, R.D. (2006). Knowledge-intensive Collaborative Decision Support for Design Process. In: Intelligent Decision-making Support Systems. Decision Engineering. Springer, London. https://doi.org/10.1007/1-84628-231-4_16
Download citation
DOI: https://doi.org/10.1007/1-84628-231-4_16
Publisher Name: Springer, London
Print ISBN: 978-1-84628-228-7
Online ISBN: 978-1-84628-231-7
eBook Packages: EngineeringEngineering (R0)