|Start Page Number:||607|
|End Page Number:||625|
|Publication Date:||Jul 2017|
|Journal:||RAIRO - Operations Research|
|Authors:||Carrabs Francesco, Raiconi Andrea, Cerulli Raffaele, DAmbrosio Ciriaco|
|Keywords:||combinatorial optimization, optimization, heuristics, scheduling, heuristics: genetic algorithms|
The aim of the Connected Maximum Lifetime Problem is to define a schedule for the activation intervals of the sensors deployed inside a region of interest, such that at all times the activated sensors can monitor a set of interesting target locations and route the collected information to a central base station, while maximizing the total amount of time over which the sensor network can be operational. Complete or partial coverage of the targets are taken into account. To optimally solve the problem, we propose a column generation approach which makes use of an appropriately designed genetic algorithm to overcome the difficulty of solving the subproblem to optimality in each iteration. Moreover, we also devise a heuristic by stopping the column generation procedure as soon as the columns found by the genetic algorithm do not improve the incumbent solution. Comparisons with previous approaches proposed in the literature show our algorithms to be highly competitive, both in terms of solution quality and computational time.