Journal of Optimization Theory and Applications
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
A deterministic algorithm for global optimization
Mathematical Programming: Series A and B
LEDA: a platform for combinatorial and geometric computing
Communications of the ACM
LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
Optimization of the norm of a vector-valued DC function and applications
Journal of Optimization Theory and Applications
Weber‘s Problem with Attraction and Repulsion under Polyhedral Gauges
Journal of Global Optimization
Epsilon Dominance and Constraint Partitioningin Multiple Objective Problems
Journal of Global Optimization
On Covering Methods for D.C. Optimization
Journal of Global Optimization
Euclidean push-pull partial covering problems
Computers and Operations Research
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Locating Objects in the Plane Using Global Optimization Techniques
Mathematics of Operations Research
Approximating the Ɛ-efficient set of an MOP with stochastic search algorithms
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
On minimax-regret Huff location models
Computers and Operations Research
Locating a semi-obnoxious covering facility with repelling polygonal regions
Discrete Applied Mathematics
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In this paper we address the biobjective problem of locating a semiobnoxious facility, that must provide service to a given set of demand points and, at the same time, has some negative effect on given regions in the plane. In the model considered, the location of the new facility is selected in such a way that it gives answer to these contradicting aims: minimize the service cost (given by a quite general function of the distances to the demand points) and maximize the distance to the nearest affected region, in order to reduce the negative impact. Instead of addressing the problem following the traditional trend in the literature (i.e., by aggregation of the two objectives into a single one), we will focus our attention in the construction of a finite ϵ-dominating set, that is, a finite feasible subset that approximates the Pareto-optimal outcome for the biobjective problem. This approach involves the resolution of univariate d.c. optimization problems, for each of which we show that a d.c. decomposition of its objective can be obtained, allowing us to use standard d.c. optimization techniques.