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Toward More Realistic Allocation in Location—Allocation Models: An Interaction Approach

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  • M J Hodgson

    (Department of Geography, The University of Alberta, Edmonton, Canada T6G 2H4)

Abstract

Location—allocation models jointly specify the optimal locations of service facilities and the allocation of patrons to them. Almost without exception, the allocation rule employed in these models has assumed that patrons wish to use the facility at the least travel cost away from them. Spatial interaction theory suggests that a person's travel behaviour is influenced by many other factors, among them differential facility attractiveness and uncertainty about travel costs, and that the least-cost allocation rule is unrealistic. This paper presents a location—allocation model employing an entropy-maximizing interaction model to allocate patrons to facilities. A heuristic solution procedure is proposed and found to be effective and reasonably efficient for small problems. Insofar as travel behaviour in the system is suboptimal, the location—allocation model produces suboptimal solutions. In the interests of providing realistic solutions to real-world problems, however, it is essential that planners accommodate the behaviour of those they plan for, be it normative or not.

Suggested Citation

  • M J Hodgson, 1978. "Toward More Realistic Allocation in Location—Allocation Models: An Interaction Approach," Environment and Planning A, , vol. 10(11), pages 1273-1285, November.
    • Handle: RePEc:sae:envira:v:10:y:1978:i:11:p:1273-1285
    DOI: 10.1068/a101273
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    References listed on IDEAS

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    1. William J. Abernathy & John C. Hershey, 1972. "A Spatial-Allocation Model for Regional Health-Services Planning," Operations Research, INFORMS, vol. 20(3), pages 629-642, June.
    2. David L. Huff, 1966. "A Programmed Solution for Approximating an Optimum Retail Location," Land Economics, University of Wisconsin Press, vol. 42(3), pages 293-303.
    3. CORNUEJOLS, Gérard & FISHER, Marshall L. & NEMHAUSER, George L., 1977. "Location of bank accounts to optimize float: An analytic study of exact and approximate algorithms," LIDAM Reprints CORE 292, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. James F. Campbell & Morton E. O'Kelly, 2012. "Twenty-Five Years of Hub Location Research," Transportation Science, INFORMS, vol. 46(2), pages 153-169, May.
    2. Gunhak Lee & Morton E. O'Kelly, 2009. "Exploring Locational Equilibria In A Competitive Broadband Access Market: Theoretical Modeling Approach," Journal of Regional Science, Wiley Blackwell, vol. 49(5), pages 953-975, December.
    3. Vladimir Marianov & H. A. Eiselt, 2016. "On agglomeration in competitive location models," Annals of Operations Research, Springer, vol. 246(1), pages 31-55, November.
    4. Mauricio Resende & Renato Werneck, 2007. "A fast swap-based local search procedure for location problems," Annals of Operations Research, Springer, vol. 150(1), pages 205-230, March.
    5. M. Hodgson & Soren Jacobsen, 2009. "A hierarchical location-allocation model with travel based on expected referral distances," Annals of Operations Research, Springer, vol. 167(1), pages 271-286, March.
    6. Carling, Kenneth & Han, Mengjie & Håkansson, Johan & Rebreyend, Pascal, 2015. "Testing the gravity p-median model empirically," Operations Research Perspectives, Elsevier, vol. 2(C), pages 124-132.

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