Fast heuristics for large scale covering-location problems
Computers and Operations Research - Location analysis
A maximal covering location model in the presence of partial coverage
Computers and Operations Research
Coverage models in military operations
CIT'11 Proceedings of the 5th WSEAS international conference on Communications and information technology
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The representation of demand is a key issue which can significantly affect results in several demand covering models. In this paper we concentrate on the well known Maximal Coverage Location Problem and introduce a new parametrical object oriented approach for the discretization process so as to demonstrate that alternative representations of the demand space during the discertization process may lead to largely fluctuating as well as misleading results which seriously overestimate or underestimate the real coverage achieved by a specified number of servers. The new parametrical object oriented approach is based on the notions of data abstraction, encapsulation, polymorphism and inheritance and exploits the capabilities of Geographic Information Systems so as to create flexibility during the representation of demand. Furthermore we modify MCLP so as to work more efficiently with the new discertization process using exclusively objects and not characteristics like distance. Results of an empirical study indicate that the combination of the new object oriented approach with the modified MCLP is an alternative way so as not to create overestimation or underestimation and that there is absolutely no difference between the real (actual) coverage that can be verified using ArcGIS tools and the reported coverage, given by the solution of each integer problem.