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Providing Design Assistance: A Case-Based Approach

Author

Listed:
  • Atish P. Sinha

    (Department of MIS & Decision Sciences, University of Dayton, Dayton, Ohio 45469-2130)

  • Jerrold H. May

    (Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

Abstract

This paper presents an integrated and comprehensive framework for decision support. A model integrating case-based reasoning with constraint posting and multicriteria decision making is proposed for providing effective and efficient assistance in solving routine design problems. The model is developed based on an analysis of the knowledge acquired from experts in engineering design, and is subsequently operationalized as a computer-based design assistant called IDEA. IDEA employs constraint posting to initially bound the design space and to maintain consistency of the design solutions. Case-based reasoning allows IDEA to generate new designs by retrieving, adapting, and composing from similar cases in memory. Finally, IDEA optimizes multiple objectives to identify a set of pareto-optimal designs. By organizing computer memory as a collection of cases and case snippets, and by adapting and synthesizing those cases and snippets---using techniques similar to those employed by design experts---IDEA provides valuable design assistance. In addition to providing a framework for decision support, the research makes specific contributions to case-based design. It shows how case snippets can be retrieved, adapted, and synthesized to generate multiple design solutions, whose consistency is enforced through a dynamic constraint management mechanism. The concepts and techniques developed for performing dynamic adaptation (adaptation during composition from case snippets) and for maintaining an evolving solution space (a solution space that shrinks and expands over time) contribute to the state-of-the-art in case-based design.

Suggested Citation

  • Atish P. Sinha & Jerrold H. May, 1996. "Providing Design Assistance: A Case-Based Approach," Information Systems Research, INFORMS, vol. 7(3), pages 363-387, September.
  • Handle: RePEc:inm:orisre:v:7:y:1996:i:3:p:363-387
    DOI: 10.1287/isre.7.3.363
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    Cited by:

    1. Victor P. Seidel & Christoph Riedl, 2023. "How creative versus technical constraints affect individual learning in an online innovation community," Papers 2303.15163, arXiv.org.
    2. Hu, Qiwei & Chakhar, Salem & Siraj, Sajid & Labib, Ashraf, 2017. "Spare parts classification in industrial manufacturing using the dominance-based rough set approach," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1136-1163.

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