Deterministic algorithm with agglomerative heuristic for location problems

Authors consider the clustering problem solved with the k-means method and p-median problem with various distance metrics. The p-median problem and the k-means problem as its special case are most popular models of the location theory. They are implemented for solving problems of clustering and many...

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Published inIOP conference series. Materials Science and Engineering Vol. 94; no. 1; pp. 12016 - 12024
Main Authors Kazakovtsev, L, Stupina, A
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 2015
Subjects
Agglomeration
Algorithms
Boilers
Clustering
Gas wells
Genetic algorithms
Greedy algorithms
Heuristic
Heuristic methods
Materials science
Oil fields
Optimization
Problem solving
Site selection
Online AccessGet full text
ISSN1757-8981
1757-899X
1757-899X
DOI10.1088/1757-899X/94/1/012016

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Abstract Authors consider the clustering problem solved with the k-means method and p-median problem with various distance metrics. The p-median problem and the k-means problem as its special case are most popular models of the location theory. They are implemented for solving problems of clustering and many practically important logistic problems such as optimal factory or warehouse location, oil or gas wells, optimal drilling for oil offshore, steam generators in heavy oil fields. Authors propose new deterministic heuristic algorithm based on ideas of the Information Bottleneck Clustering and genetic algorithms with greedy heuristic. In this paper, results of running new algorithm on various data sets are given in comparison with known deterministic and stochastic methods. New algorithm is shown to be significantly faster than the Information Bottleneck Clustering method having analogous preciseness.
AbstractList Authors consider the clustering problem solved with the k-means method and p-median problem with various distance metrics. The p-median problem and the k-means problem as its special case are most popular models of the location theory. They are implemented for solving problems of clustering and many practically important logistic problems such as optimal factory or warehouse location, oil or gas wells, optimal drilling for oil offshore, steam generators in heavy oil fields. Authors propose new deterministic heuristic algorithm based on ideas of the Information Bottleneck Clustering and genetic algorithms with greedy heuristic. In this paper, results of running new algorithm on various data sets are given in comparison with known deterministic and stochastic methods. New algorithm is shown to be significantly faster than the Information Bottleneck Clustering method having analogous preciseness.
Author Stupina, A
Kazakovtsev, L
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SubjectTerms Agglomeration
Algorithms
Boilers
Clustering
Gas wells
Genetic algorithms
Greedy algorithms
Heuristic
Heuristic methods
Materials science
Oil fields
Optimization
Problem solving
Site selection
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