Hybrid heuristics for the probabilistic maximal covering location-allocation problem

Abstract

The Maximal Covering Location Problem (MCLP) maximizes the population that has a facility within a maximum travel distance or time. Numerous extensions have been proposed to enhance its applicability, like the probabilistic model for the maximum covering locationallocation with constraint in waiting time or queue length for congested systems, with one or more servers per service center. This paper presents one solution procedure for that probabilistic model, considering one server per center, using a Hybrid Heuristic known as Clustering Search (CS), that consists of detecting promising search areas based on clustering. The computational tests provide results for network instances with up to 818 vertices.

Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data

“…Therefore, the capacity limit is a crucial consideration in location problems, especially in the placement of EMS facilities. Efforts have been made to solve this constraint through probabilistic modelling approaches (Daskin, 1983;de Assis Corrêa et al, 2009;Marianov & Serra, 1998). These models assign a probability of unavailability to facilities that reduce or prevent unlimited service delivery (Galvão et al, 2005).…”