Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Dynamic Additively Weighted Voronoi Diagrams in 2D
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Artificial Intelligence Illuminated
Artificial Intelligence Illuminated
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Information Processing Letters
Exception representation and management in open multi-agent systems
Information Sciences: an International Journal
A sweepline algorithm for Euclidean Voronoi diagram of circles
Computer-Aided Design
Agent-based simulation of competitive and collaborative mechanisms for mobile service chains
Information Sciences: an International Journal
Autonomic tracing of production processes with mobile and agent-based computing
Information Sciences: an International Journal
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
Convex hull and voronoi diagram of additively weighted points
ESA'05 Proceedings of the 13th annual European conference on Algorithms
Solving the k-influence region problem with the GPU
Information Sciences: an International Journal
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In this paper, we introduce a novel class of facility location problems, and propose solutions based on Voronoi diagrams. Our solutions locate a set of facilities on a two dimensional space, with respect to a set of dynamic demand. The information about these demand is gathered through modifications of the overall system, into a central decision unit. This influences our objective of minimizing the total loss function. Considering a continuous space and discrete time, facilities are assigned to meet demands in each time cycle. Two distinct approaches are proposed and thoroughly studied, followed by a case study. We call our main algorithm Reactive Agent Dynamic Voronoi Diagram Facility Spread. We also test our solutions empirically through a set of experiments. Considering n and p to be the number of demand points, and the number of facilities in hand, respectively, the time complexity of the algorithm is O(c(n^2+p)logn) for a complete run of c cycles.