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Strategies for Solving Large Location-Allocation Problems by Heuristic Methods

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  • P J Densham

    (National Center for Geographic Information and Analysis, and Department of Geography, State University of New York at Buffalo, Buffalo, NY 14261, USA)

  • G Rushton

    (Department of Geography, San Diego State University, San Diego, CA 92182, USA)

Abstract

Solution techniques for location-allocation problems usually are not a part of microcomputer-based geoprocessing systems because of the large volumes of data to process and store and the complexity of algorithms. In this paper, it is shown that processing costs for the most accurate, heuristic, location-allocation algorithm can be drastically reduced by exploiting the spatial structure of location-allocation problems. The strategies used, preprocessing interpoint distance data as both candidate and demand strings, and use of them to update an allocation table, allow the solution of large problems (3000 nodes) in a microcomputer-based, interactive decisionmaking environment. Moreover, these strategies yield solution times which increase approximately linearly with problem size. Tests on four network problems validate these claims.

Suggested Citation

  • P J Densham & G Rushton, 1992. "Strategies for Solving Large Location-Allocation Problems by Heuristic Methods," Environment and Planning A, , vol. 24(2), pages 289-304, February.
    • Handle: RePEc:sae:envira:v:24:y:1992:i:2:p:289-304
    DOI: 10.1068/a240289
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    Cited by:

    1. W A V Clark, 1993. "Applying our Understanding: Social Science in Government and the Marketplace," Environment and Planning A, , vol. 25(1_suppl), pages 38-47, January.
    2. Upchurch, Christopher & Kuby, Michael, 2010. "Comparing the p-median and flow-refueling models for locating alternative-fuel stations," Journal of Transport Geography, Elsevier, vol. 18(6), pages 750-758.
    3. Ron Janssen & Marjan van Herwijnen & Theodor J Stewart & Jeroen C J H Aerts, 2008. "Multiobjective Decision Support for Land-Use Planning," Environment and Planning B, , vol. 35(4), pages 740-756, August.
    4. Kayode J. Samuel, 2010. "Infrastructure Location," Journal of Infrastructure Development, India Development Foundation, vol. 2(1), pages 71-90, June.
    5. Rosing, K. E. & ReVelle, C. S. & Rolland, E. & Schilling, D. A. & Current, J. R., 1998. "Heuristic concentration and Tabu search: A head to head comparison," European Journal of Operational Research, Elsevier, vol. 104(1), pages 93-99, January.
    6. Rosing, K. E. & ReVelle, C. S. & Schilling, D. A., 1999. "A gamma heuristic for the p-median problem," European Journal of Operational Research, Elsevier, vol. 117(3), pages 522-532, September.
    7. Juan Antonio Araiza-Aguilar & Constantino Gutiérrez-Palacios & María Neftalí Rojas-Valencia & Hugo Alejandro Nájera-Aguilar & Rubén Fernando Gutiérrez-Hernández & Rodrigo Antonio Aguilar-Vera, 2019. "Selection of Sites for the Treatment and the Final Disposal of Construction and Demolition Waste, Using Two Approaches: An Analysis for Mexico City," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
    8. Rosing, K. E. & ReVelle, C. S., 1997. "Heuristic concentration: Two stage solution construction," European Journal of Operational Research, Elsevier, vol. 97(1), pages 75-86, February.
    9. Vinod K. Tewari, 1992. "Improving Access to Services and Facilities in Developing Countries," International Regional Science Review, , vol. 15(1), pages 25-37, April.

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