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Locating Tax Facilities: A Graphics-Based Microcomputer Optimization Model

Author

Listed:
  • Paul D. Domich

    (Computing and Applied Mathematics Laboratory, National Institute of Standards and Technology, Boulder, Colorado 80303-3328)

  • Karla L. Hoffman

    (George Mason University, Operations Research and Applied Statistics Department, Fairfax, Virginia 22030)

  • Richard H. F. Jackson

    (Manufacturing Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899)

  • Marjorie A. McClain

    (Computing and Applied Mathematics Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899)

Abstract

This paper presents a mathematical model that selects locations for Internal Revenue Service Posts-of-Duty. The system is microcomputer-based and uses menus and graphically displayed zip code maps of IRS districts for interactive inputs and solution outputs. The mathematical model used for this problem is the uncapacitated, fixed charge, location-allocation model which minimizes travel and facility costs, given a specified level of activity. A greedy-interchange heuristic is used to obtain "good" solutions to the problem and is coupled with a lagrangian-relaxation technique providing a measure of how far from optimality the current solution can be. An example with Florida data illustrates the use of the model.

Suggested Citation

  • Paul D. Domich & Karla L. Hoffman & Richard H. F. Jackson & Marjorie A. McClain, 1991. "Locating Tax Facilities: A Graphics-Based Microcomputer Optimization Model," Management Science, INFORMS, vol. 37(8), pages 960-979, August.
    • Handle: RePEc:inm:ormnsc:v:37:y:1991:i:8:p:960-979
    DOI: 10.1287/mnsc.37.8.960
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    Citations

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    Cited by:

    1. R.L. Francis & T.J. Lowe & M.B. Rayco & A. Tamir, 2003. "Exploiting self‐canceling demand point aggregation error for some planar rectilinear median location problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(6), pages 614-637, September.
    2. R. Francis & T. Lowe & M. Rayco & A. Tamir, 2009. "Aggregation error for location models: survey and analysis," Annals of Operations Research, Springer, vol. 167(1), pages 171-208, March.
    3. R. L. Francis & T. J. Lowe & Arie Tamir, 2000. "Aggregation Error Bounds for a Class of Location Models," Operations Research, INFORMS, vol. 48(2), pages 294-307, April.
    4. Avella, P. & Benati, S. & Canovas Martinez, L. & Dalby, K. & Di Girolamo, D. & Dimitrijevic, B. & Ghiani, G. & Giannikos, I. & Guttmann, N. & Hultberg, T. H. & Fliege, J. & Marin, A. & Munoz Marquez, , 1998. "Some personal views on the current state and the future of locational analysis," European Journal of Operational Research, Elsevier, vol. 104(2), pages 269-287, January.

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