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Decision Trees with Single and Multiple Interval-Valued Objectives

Author

Listed:
  • Kash Barker

    (School of Industrial and Systems Engineering, University of Oklahoma, Norman, Oklahoma 73019)

  • Kaycee J. Wilson

    (School of Industrial and Systems Engineering, University of Oklahoma, Norman, Oklahoma 73019)

Abstract

Important to decision making is recognition of what today's decisions have on future options, and an oft-used tool to aid in this problem is the decision tree. This paper addresses situations in which uncertainty arises in the objectives associated with different sequential decision paths and provides a decision tree for uncertain parameters where only bounds, not distributions, are known. Single- and multiple-objective interval-valued decision trees are introduced. Interval arithmetic is used for the decision tree rollback process. To address the difficulty of an interval-valued comparison of alternatives, several decision rules, as well as a probabilistic approach, are discussed in the decision tree context. The interval-valued decision trees are deployed in a simple maintenance, repair, and overhaul decision-making illustration.

Suggested Citation

  • Kash Barker & Kaycee J. Wilson, 2012. "Decision Trees with Single and Multiple Interval-Valued Objectives," Decision Analysis, INFORMS, vol. 9(4), pages 348-358, December.
  • Handle: RePEc:inm:ordeca:v:9:y:2012:i:4:p:348-358
    DOI: 10.1287/deca.1120.0253
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    References listed on IDEAS

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    Cited by:

    1. Bogumił Kamiński & Michał Jakubczyk & Przemysław Szufel, 2018. "A framework for sensitivity analysis of decision trees," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 135-159, March.
    2. Yongzhi Cao, 2014. "Reducing Interval-Valued Decision Trees to Conventional Ones: Comments on Decision Trees with Single and Multiple Interval-Valued Objectives," Decision Analysis, INFORMS, vol. 11(3), pages 204-212, September.
    3. L. Robin Keller & Ali Abbas & J. Eric Bickel & Vicki M. Bier & David V. Budescu & John C. Butler & Enrico Diecidue & Robin L. Dillon-Merrill & Raimo P. Hämäläinen & Kenneth C. Lichtendahl & Jason R. W, 2012. "From the Editors ---Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus," Decision Analysis, INFORMS, vol. 9(4), pages 297-302, December.
    4. Sarat Sivaprasad & Cameron A. MacKenzie, 2018. "The Hurwicz Decision Rule’s Relationship to Decision Making with the Triangle and Beta Distributions and Exponential Utility," Decision Analysis, INFORMS, vol. 15(3), pages 139-153, September.

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