Approximating the Pareto frontier for a challenging real-world bi-objective covering problem

We study a bi-objective covering problem stemming from real-world application concerning the design of camera surveillance systems for large-scale outdoor areas. It is in this prohibitively costly to surveil entire area, and therefore necessary be able present decision-maker with trade-offs between total cost portion area that surveilled. The can stated as set two objectives, describing constraints are fulfilled. Finding Pareto frontier these objectives very computationally demanding we derive method finding good approximate practically feasible computing time. based on ϵ-constraint reformulation, an established heuristic problems, subgradient optimization.