An overview of representative problems in location research
Management Science
The maximal covering location problem with capacities on total workload
Management Science
A multiperiod set covering location model for dynamic redeployment of ambulances
Computers and Operations Research
A decision support system for locating VHF/UHF radio jammer systems on the terrain
Information Systems Frontiers
Planar maximal covering with ellipses
Computers and Industrial Engineering
A mixed integer linear program and tabu search approach for the complementary edge covering problem
Advances in Engineering Software
Review: Generalized coverage: New developments in covering location models
Computers and Operations Research
Tabu based heuristics for the generalized hierarchical covering location problem
Computers and Industrial Engineering
The Mobile Facility Routing Problem
Transportation Science
Survey: Covering problems in facility location: A review
Computers and Industrial Engineering
An object oriented approach for the discertization process
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
Facility location and scale decision problem with customer preference
Computers and Industrial Engineering
Maximal covering location problem (MCLP) with fuzzy travel times
Expert Systems with Applications: An International Journal
A decision support system for locating weapon and radar positions in stationary point air defence
Information Systems Frontiers
Humanitarian/emergency logistics models: a state of the art overview
Proceedings of the 2013 Summer Computer Simulation Conference
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The maximal covering location problem (MCLP) addresses the issue of locating a predefined number of facilities in order to maximize the number of demand points that can be covered. In a classical sense, a demand point is assumed to be covered completely if located within the critical distance of the facility and not covered at all outside of the critical distance. Since the optimal solution to a MCLP is likely sensitive to the choice of the critical distance, determining a critical distance value when the coverage does not change in a crisp way from "fully covered" to "not covered" at a specific distance may lead to erroneous results. We allow the coverage to change from "covered" to "not-covered" within a distance range instead of a single critical distance and call this intermediate coverage level partial coverage. In this paper, we formulate the MCLP in the presence of partial coverage, develop a solution procedure based on Lagrangean relaxation and show the effect of the approach on the optimal solution by comparing it with the classical approach.