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Quad Tree Data Structures For Use In Large–Scale Discrete Alternative Multiple Criteria Problems

In: New Frontiers Of Decision Making For The Information Technology Era

Author

Listed:
  • MINGHE SUN

    (College of Business, The University of Texas at San Antonio, San Antonio, TX 78249-0634, USA)

  • RALPH E. STEUER

    (Terry College of Business, The University of Georgia, Athens, GA 30602-6253, USA)

Abstract

This paper addresses the problem of identifying, storing, and retrieving nondominated criterion vectors with a quad tree data structure in support of procedures for solving multiple criteria problems whose feasible regions consist of large numbers of discrete alternatives. In procedures for such problems, two tasks are encountered. In the first, nondominated criterion vectors are to be distinguished and stored in a quad tree. In the second, nondominated criterion vectors in the quad tree are to be retrieved in the search for the decision maker's optimal solution. In this paper, each criterion vector retrieved is the nondominated criterion vector that is closest to a utopian point according to a λ-weighted Tchebycheff metric. Examples are provided and computational results for large-scale problems are reported.

Suggested Citation

  • Minghe Sun & Ralph E. Steuer, 2000. "Quad Tree Data Structures For Use In Large–Scale Discrete Alternative Multiple Criteria Problems," World Scientific Book Chapters, in: Yong Shi & Milan Zeleny (ed.), New Frontiers Of Decision Making For The Information Technology Era, chapter 4, pages 48-71, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812792907_0004
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