Regrouping service sites: a genetic approach using a voronoi diagram

Authors:
Jeong-Yeon Seo;Sang-Min Park;Seoung Soo Lee;Deok-Soo Kim
Affiliations:
Department of Industrial Engineering, Hanyang University, Seoul, South Korea;Voronoi Diagram Research Center, Hanyang University, Seoul, South Korea;CAESIT, Konkuk University, Seoul, South Korea;Department of Industrial Engineering, Hanyang University, Seoul, South Korea
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Year:
2005

Citing 10
Cited 0

Voronoi diagrams—a survey of a fundamental geometric data structure

ACM Computing Surveys (CSUR)
Spatial tessellations: concepts and applications of Voronoi diagrams

Spatial tessellations: concepts and applications of Voronoi diagrams
Approximation schemes for Euclidean k-medians and related problems

STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Approximation algorithms

Approximation algorithms
Practical Handbook of Genetic Algorithms

Practical Handbook of Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Genetic Algorithm and Graph Partitioning

IEEE Transactions on Computers
Voronoi Quantizied Crossover For Traveling Salesman Problem

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A genetic algorithm approach for regrouping service sites

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we consider the problem of regrouping service sites into a smaller number of service sites called centers. Each service site is represented as a point in the plane and has service demand. We aim to group the sites so that each group has balanced service demand and the sum of distances between sites and their corresponding center is minimized. By using Voronoi diagrams, we obtain topological information among the sites and based on this, we define a mutation operator of a genetic algorithm. The experimental results show improvements in running time as well as cost optimization. We also provide a variety of empirical results by changing the relative importance of the two criteria, which involve service demand and distances, respectively.