The Maximal Cover Location Model with Hedging

Sam Ratick, Jeffrey Osleeb and Kangping Si

International Regional Science Review, 2016, vol. 39, issue 1, 77-107

Abstract: The maximal covering location problem (MCLP) model and the large number of applications and modifications that have emanated from it have been extensively used to site facility networks in a wide variety of applications. In this article, we formulate and apply an extension of MCLP, the Maximal Covering Location Problem with Hedging (MCLPH), to address the problem of siting facilities when the demand for service from those facilities is uncertain. The MCLPH model treats the maximal cover of different potential demand populations in the system as different objectives for the MCLP, with some lexicographic ordering of objectives related to the degree of uncertainty about the sizes and spatial pattern of those demands. We apply the MCLPH model to the problem of designing a medical network of screening facilities for people who may have been exposed to lead contamination in the Dominican Republic (DR). In the DR, there are three suspected sources of lead contamination, waterborne lead from runoff as a result of gold mining activities, airborne lead contamination from the emissions of a battery recycling plant, and airborne lead from the use of leaded gasoline in transportation. The geographical patterns of contamination from these three sources are different and therefore, the populations of the cities and towns in the DR can be expected to be differentially exposed depending upon which is the actual source of the lead. A geographical information system-based hazard analysis is used to provide input data to the MCLPH and to display and evaluate the resulting facility location patterns.

Keywords: location models; spatial analysis; optimization; multi-objective and multi-criteria analysis; geographic information science; health care; policy and applications (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:inrsre:v:39:y:2016:i:1:p:77-107

DOI: 10.1177/0160017615576080

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