SwePub - search: WFRF:(Ngulo Uledi 1983 )

Enumeration	Reference	Cover	Find
1.	Quttineh, Nils-Hassan, 1979-, et al. (author) Approximating the Pareto frontier for a challenging real-world bi-objective covering problem 2022 In: INFOR. Information systems and operational research. - : Taylor & Francis Inc. - 0315-5986 .- 1916-0615. ; 60:3, s. 342-358 Journal article (peer-reviewed)abstract We study a bi-objective covering problem stemming from a real-world application concerning the design of camera surveillance systems for large-scale outdoor areas. It is in this application prohibitively costly to surveil the entire area, and therefore necessary to be able to present a decision-maker with trade-offs between total cost and the portion of the area that is surveilled. The problem can be stated as a set covering problem with two objectives, describing cost and portion of covering constraints that are fulfilled. Finding the Pareto frontier for these objectives is very computationally demanding and we therefore derive a method for finding a good approximate frontier in a practically feasible computing time. The method is based on the epsilon-constraint reformulation, an established heuristic for set covering problems, and subgradient optimization.

Quttineh, Nils-Hassan, 1979-, et al. (author)
Approximating the Pareto frontier for a challenging real-world bi-objective covering problem
2022
In: INFOR. Information systems and operational research. - : Taylor & Francis Inc. - 0315-5986 .- 1916-0615. ; 60:3, s. 342-358
Journal article (peer-reviewed)abstract
- We study a bi-objective covering problem stemming from a real-world application concerning the design of camera surveillance systems for large-scale outdoor areas. It is in this application prohibitively costly to surveil the entire area, and therefore necessary to be able to present a decision-maker with trade-offs between total cost and the portion of the area that is surveilled. The problem can be stated as a set covering problem with two objectives, describing cost and portion of covering constraints that are fulfilled. Finding the Pareto frontier for these objectives is very computationally demanding and we therefore derive a method for finding a good approximate frontier in a practically feasible computing time. The method is based on the epsilon-constraint reformulation, an established heuristic for set covering problems, and subgradient optimization.

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