QUAD TREE DATA STRUCTURES FOR USE IN LARGE–SCALE DISCRETE ALTERNATIVE MULTIPLE CRITERIA PROBLEMS

Minghe Sun and Ralph E. Steuer
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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

Chapter 4 in New Frontiers of Decision Making for the Information Technology Era, 2000, pp 48-71 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractThis 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.

Keywords: Multiple Criteria; Decision Making; Decision Support Systems; Tradeoff Analysis; System Engineering; Linear Programming (search for similar items in EconPapers)
Date: 2000
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