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On a multimode test sequencing problem.

Sui Ruan1, Fang Tu, Krishna R Pattipati

  • 1Electrical and Computer Engineering Department, University of Connecticut, Storrs, CT 06269-1157, USA. sruan@engr.uconn.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 16, 2004
PubMed
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This study introduces heuristic algorithms to solve complex multimode test sequencing problems efficiently. Rollout strategies combined with information gain heuristics significantly improve performance over traditional methods.

Area of Science:

  • Engineering
  • Computer Science
  • Operations Research

Background:

  • Test sequencing aims to minimize costs in identifying system failure states.
  • Multimode Test Sequencing (MMTS) introduces complexities with mode transitions and associated costs.
  • Traditional optimal methods like dynamic programming face computational challenges for large systems.

Purpose of the Study:

  • To develop efficient heuristic algorithms for the MMTS problem.
  • To overcome computational complexity associated with optimal methods for large-scale MMTS.
  • To evaluate the performance of information gain-based heuristics and rollout strategies.

Main Methods:

  • Proposed three heuristic algorithms: Information Gain (IG), Mode Capability Evaluation (MC), and MC with Limited Exploration (MCLEI).

Related Experiment Videos

  • Applied rollout strategies to enhance heuristic performance.
  • Compared heuristic performance against traditional methods using computational experiments.

    • Main Results:

    • Information-heuristic based rollout policies demonstrated significant improvements over traditional information gain heuristics.
    • MCLEI offered the best balance between solution optimality and computational efficiency among the proposed heuristics.
    • Heuristic approaches effectively addressed the computational explosion seen in optimal methods for large MMTS problems.

    Conclusions:

    • Heuristic algorithms, particularly MCLEI, provide effective solutions for the MMTS problem.
    • Rollout strategies enhance the performance of information gain-based heuristics.
    • The proposed methods offer a practical approach to complex test sequencing in large systems.