Deterministic algorithm with agglomerative heuristic for location problems

• Published in: IOP conference series. Materials Science and Engineering Vol. 94; no. 1; pp. 12016 - 12024
• Main Authors: Kazakovtsev, L, Stupina, A
• Format: Journal Article
• Language: English
• 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
• ISSN: 1757-8981; 1757-899X; 1757-899X
• DOI: 10.1088/1757-899X/94/1/012016

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.