TY - JOUR
T1 - Linear formulation for the Maximum Expected Coverage Location Model with fractional coverage
AU - van den Berg, P.L.
AU - Kommer, G.J.
AU - Zuzáková, B.
PY - 2016
Y1 - 2016
N2 - Since ambulance providers are responsible for life-saving medical care at the scene in emergency situations and since response times are important in these situations, it is crucial that ambulances are located in such a way that good coverage is provided throughout the region. Most models that are developed to determine good base locations assume strict 0-1 coverage given a fixed base location and demand point. However, multiple applications require fractional coverage. Examples include stochastic, instead of fixed, response times and survival probabilities. Straightforward adaption of the well-studied MEXCLP to allow for coverage probabilities results in a non-linear formulation in integer variables, limiting the size of instances that can be solved by the model. In this paper, we present a linear integer programming formulation for the problem. We show that the computation time of the linear formulation is significantly shorter than that for the non-linear formulation. As a consequence, we are able to solve larger instances. Finally, we will apply the model, in the setting of stochastic response times, to the region of Amsterdam, the Netherlands.
AB - Since ambulance providers are responsible for life-saving medical care at the scene in emergency situations and since response times are important in these situations, it is crucial that ambulances are located in such a way that good coverage is provided throughout the region. Most models that are developed to determine good base locations assume strict 0-1 coverage given a fixed base location and demand point. However, multiple applications require fractional coverage. Examples include stochastic, instead of fixed, response times and survival probabilities. Straightforward adaption of the well-studied MEXCLP to allow for coverage probabilities results in a non-linear formulation in integer variables, limiting the size of instances that can be solved by the model. In this paper, we present a linear integer programming formulation for the problem. We show that the computation time of the linear formulation is significantly shorter than that for the non-linear formulation. As a consequence, we are able to solve larger instances. Finally, we will apply the model, in the setting of stochastic response times, to the region of Amsterdam, the Netherlands.
KW - Ambulance base locations
KW - Integer linear programming models
KW - Location analysis
UR - http://www.scopus.com/inward/record.url?scp=84941255102&partnerID=8YFLogxK
U2 - 10.1016/j.orhc.2015.08.001
DO - 10.1016/j.orhc.2015.08.001
M3 - Article
AN - SCOPUS:84941255102
SN - 2211-6923
VL - 8
SP - 33
EP - 41
JO - Operations Research for Health Care
JF - Operations Research for Health Care
ER -