Concepts and applications of backup coverage
Management Science
The capacitated maximal covering location problem with backup service
Annals of Operations Research
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
An improved branch and bound algorithm for mixed integer nonlinear programs
Computers and Operations Research
ENCON: an evolutionary algorithm for the antenna placement problem
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
The solution of nonlinear pseudo-boolean optimization problems subject to linear constraints
The solution of nonlinear pseudo-boolean optimization problems subject to linear constraints
On the optimality of facility location for wireless transmission infrastructure
Computers and Industrial Engineering
An efficient heuristic for the expansion problem of cellular wireless networks
Computers and Operations Research
Production and location on a network under demand uncertainty
Operations Research Letters
Computers and Industrial Engineering
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We offer a variant of the maximal covering location problem to locate up to p signal-receiving stations. The ''demands,'' called geolocations, to be covered by these stations are distress signals and/or transmissions from any targets. The problem is complicated by several factors. First, to find a signal location, the signal must be received by at least three stations-two lines of bearing for triangulation and a third for accuracy. Second, signal frequencies vary by source and the included stations do not necessarily receive all frequencies. One must decide which listening frequencies are allocated to which stations. Finally, the range or coverage area of a station varies stochastically because of meteorological conditions. This problem is modeled using a multiobjective (or multicriteria) linear integer program (MOLIP), which is an approximation of a highly nonlinear integer program. As a solution algorithm, the MOLIP is converted to a two-stage network-flow formulation that reduces the number of explicitly enumerated integer variables. Non-inferior solutions of the MOLIP are evaluated by a value function, which identifies solutions that are similar to the more accurate nonlinear model. In all case studies, the ''best'' non-inferior solutions were about one to four standard deviations better than the sample mean of thousands of randomly located receivers with heuristic frequency assignments. We also show that a two-stage network-flow algorithm is a practical solution to an intractable nonlinear integer model. Most importantly, the procedure has been implemented in the field.