Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
ACM Computing Surveys (CSUR)
Variable Neighborhood Decomposition Search
Journal of Heuristics
Heuristic Methods for Large Centroid Clustering Problems
Journal of Heuristics
A Heuristic Method for the Set Covering Problem
Operations Research
Aggregation Error Bounds for a Class of Location Models
Operations Research
A Hybrid Heuristic for the p-Median Problem
Journal of Heuristics
Solving the p-Median Problem with a Semi-Lagrangian Relaxation
Computational Optimization and Applications
Computational study of large-scale p-Median problems
Mathematical Programming: Series A and B
A branch-and-price approach to p-median location problems
Computers and Operations Research
Dynamic supply chain design with inventory
Computers and Operations Research
Heuristic Solutions for Locating Health Resources
IEEE Intelligent Systems
Primal-Dual Variable Neighborhood Search for the Simple Plant-Location Problem
INFORMS Journal on Computing
An effective heuristic for large-scale capacitated facility location problems
Journal of Heuristics
Solving Large p-Median Problems with a Radius Formulation
INFORMS Journal on Computing
A computational study of a nonlinear minsum facility location problem
Computers and Operations Research
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The p-median problem (PMP) consists of locating p facilities (medians) in order to minimize the sum of distances from each client to the nearest facility. The interest in the large-scale PMP arises from applications in cluster analysis, where a set of patterns has to be partitioned into subsets (clusters) on the base of similarity. In this paper we introduce a new heuristic for large-scale PMP instances, based on Lagrangean relaxation. It consists of three main components: subgradient column generation, combining subgradient optimization with column generation; a ''core'' heuristic, which computes an upper bound by solving a reduced problem defined by a subset of the original variables chosen on a base of Lagrangean reduced costs; and an aggregation procedure that defines reduced size instances by aggregating together clients with the facilities. Computational results show that the proposed heuristic is able to compute good quality lower and upper bounds for instances up to 90,000 clients and potential facilities.