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Maintaining Diagnostic Knowledge-Based Systems: A Control-Theoretic Approach

Author

Listed:
  • Alain Bensoussan

    (School of Management, University of Texas at Dallas, Richardson, Texas 75083)

  • Radha Mookerjee

    (School of Management, University of Texas at Dallas, Richardson, Texas 75083)

  • Vijay Mookerjee

    (School of Management, University of Texas at Dallas, Richardson, Texas 75083)

  • Wei T. Yue

    (School of Management, University of Texas at Dallas, Richardson, Texas 75083)

Abstract

Diagnostic knowledge-based systems are used in a variety of application domains to support classification decisions. The effectiveness of such systems often decreases as the application environment or user preferences change over time. Hence, frequent adjustments to the system knowledge by a human expert become necessary. We study the problem of determining the optimal amount of effort that should be exerted to maintain the system over a planning horizon (finite or infinite). Using the receiver operating characteristic curve to derive a measure for system performance, we maximize system value by balancing system benefits with maintenance costs. The problem is cast as an optimal control model in which the goal is to choose the timing and extent of maintenance that must be expended to maximize system value. We find that the optimal solution usually possesses a steady-state component. The maintenance problem is also solved as a discrete, impulse control problem, as well as one where maintenance effort has a nonlinear impact on system performance.

Suggested Citation

  • Alain Bensoussan & Radha Mookerjee & Vijay Mookerjee & Wei T. Yue, 2009. "Maintaining Diagnostic Knowledge-Based Systems: A Control-Theoretic Approach," Management Science, INFORMS, vol. 55(2), pages 294-310, February.
    • Handle: RePEc:inm:ormnsc:v:55:y:2009:i:2:p:294-310
    DOI: 10.1287/mnsc.1080.0908
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhepeng Li & Xiao Fang & Xue Bai & Olivia R. Liu Sheng, 2017. "Utility-Based Link Recommendation for Online Social Networks," Management Science, INFORMS, vol. 63(6), pages 1938-1952, June.
    2. Xiao Fang & Olivia R. Liu Sheng & Paulo Goes, 2013. "When Is the Right Time to Refresh Knowledge Discovered from Data?," Operations Research, INFORMS, vol. 61(1), pages 32-44, February.
    3. Vijay Mookerjee & Radha Mookerjee & Alain Bensoussan & Wei T. Yue, 2011. "When Hackers Talk: Managing Information Security Under Variable Attack Rates and Knowledge Dissemination," Information Systems Research, INFORMS, vol. 22(3), pages 606-623, September.
    4. Wei Li & Wolfgang Karl Hardle & Stefan Lessmann, 2022. "A Data-driven Case-based Reasoning in Bankruptcy Prediction," Papers 2211.00921, arXiv.org.
    5. Fons Wijnhoven, 2022. "Organizational Learning for Intelligence Amplification Adoption: Lessons from a Clinical Decision Support System Adoption Project," Information Systems Frontiers, Springer, vol. 24(3), pages 731-744, June.
    6. Alain Bensoussan & Vijay Mookerjee & Wei T. Yue, 2020. "Managing Information System Security Under Continuous and Abrupt Deterioration," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1894-1917, August.

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