Elsevier

Journal of Algorithms

Volume 9, Issue 4, December 1988, Pages 583-596
Journal of Algorithms

An algorithm for the Lorenz measure in locational decisions on trees

https://doi.org/10.1016/0196-6774(88)90018-1Get rights and content

Abstract

We present an algorithm for finding an optimal location with respect to the Lorenz equity measure on a weighted tree. The algorithm complexity, O(n3log n), substantially improves a previous algorithm for the same problem. Discussion of the behavior of the optimal location shows the sensitivity and intricate nature of this measure, which may result in an unexpected optimal location under extreme conditions. Some examples illustrate these phenomena.

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    The standard statistical dispersion measures that are used as inequity measures for equitably locating facilities include range (see e.g. Brill, Liebman, & ReVelle, 1976; Erkut & Neuman, 1992), variance (see e.g. Berman, 1990; Maimon, 1986; Kincaid & Maimon, 1989), mean absolute deviation (see e.g. Berman & Kaplan, 1990; Mulligan, 1991), and sum of absolute differences (see e.g. Keeney, 1980; Lopez-de-los-Mozos & Mesa, 2001, 2003). Moreover, the Gini coefficient, which is commonly used to measure inequity of income, has been widely used in the field of equitable facility location design (Drezner, Drezner, & Guyse, 2009; Erkut, 1993; Maimon, 1988). For a review of measures for equity in facility location, see Marsh and Schilling (1994).

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