Integrating Expected Coverage and Local Reliability for Emergency Medical Services Location Problems

Published in: Socio-Economic Planning Sciences, v. 44, no. 1, Mar. 2010, p. 8-18

Posted on RAND.org on January 01, 2010

by Paul Sorensen, Richard Church

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Daskin's, The Maximum Expected Covering Location Problem (MEXCLP) model was one of the first efforts to capture the stochastic nature of emergency medical services (EMS) location problems within a mixed-integer formulation. With their subsequent introduction of MALP, Maximum Availability Location Problem, offered two key advances, local vehicle busyness estimates and the x-reliability objective. While these constructs have influenced many subsequent EMS location models, they have been subjected to relatively little empirical analysis. To address this, we introduce the LR-MEXCLP, a hybrid model combining the local busyness estimates of MALP with the maximum coverage objective of MEXCLP. We then solve a series of problems with all three models and employ simulation to estimate aggregate service levels. We find that LR-MEXCLP leads to modest but consistent service gains over both MALP and MEXCLP. These results support the merits of local busyness estimates, but they also suggest that the x-reliability objective may be inappropriate when seeking to maximize aggregate system response capabilities. More generally, our research underscores the utility of (a) linking modeling assumptions and goals with real-world application contexts, and (b) employing simulation or other techniques to validate theoretical results.

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