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The Origins of Discrete Diversity

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Discrete Diversity and Dispersion Maximization

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 204))

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Abstract

The p-dispersion facility location problem first emerged in the 1970s as a byproduct of covering and center location problems and the dual relationship between the continuous 1-center solution on a tree network and the two most distant points on the tree. Building on this insight, Shier (Transpor Sci 11(3):243–252, 1977) was the first to recognize the dispersion problem as worthy of study in its own right, with useful real-world applications such as locating oil storage tanks and retail franchises. In the 1980s, two milestones were achieved by researchers who were notably motivated by trying to solve other problems. First, Chandrasekaran and Daughety (Math Oper Res 6(1):50–57, 1981) introduced the “dispersion” nomenclature for the problem of maximizing the diversity of location of points on a tree network, motivated by the need to diversify simulation experiments for fitting a railroad cost surface (Daughety and Turnquist (Oper Res 29(3):485–500, 1981)). Second, Kuby (Geograph Analy 19(4):315–329, 1987) developed the first mixed-integer programming formulation of the discrete dispersion problem in order to generate a regularly spaced solution for his optimization model of central place theory, a geographical theory of retail and services in an urban hierarchy. Finally, Erkut and Neuman (Eur J Oper Res 40(3):275–291, 1989) and Erkut (Eur J Oper Res 46(1):48–60, 1990) popularized dispersion for locating “mutually obnoxious” types of undesirable facilities and developed faster solution methods for the discrete diversity problem.

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Notes

  1. 1.

    I would like to correct my youthful self at the bottom of p. 321 where I suggested that the maxisum dispersion problem is related to the maximin p-dispersion problem in the same way the minisum p-median problem is related to the minimax p-center problem. I was probably looking at both the median and maxisum-dispersion objective functions and seeing double summations. This analogy does not hold up, however, because the median objective counts only the allocation to the nearest facility, while the maxisum dispersion model counts all of the separation distances from all facilities to all other facilities. Ignoring the number of summations in the objective function, the p-median problem is a closer analogue to the p-defense problem, in that both objectives sum the distances to only the nearest facility.

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Acknowledgements

In the course of writing this chapter, I had the unique privilege of chatting either by Zoom or email with Douglas Shier, Ramaswamy Chandrasekaran, and Andrew Daughety about their foundational work on the p-dispersion problem. I thank them for sharing their recollections and insights, which I hope I have captured accurately here. I would also like to thank my old friend Susan Neuman for detailed copy editing of my first draft. Finally, I am grateful to co-editors Rafa Martí, for initially inviting me to contribute a chapter, and Anna Martínez-Gavara, for her very generous and much-needed assistance with formatting in Latex.

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Kuby, M. (2023). The Origins of Discrete Diversity. In: Martí, R., Martínez-Gavara, A. (eds) Discrete Diversity and Dispersion Maximization. Springer Optimization and Its Applications, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-031-38310-6_2

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