Solving a large scale districting problem: a case report
Computers and Operations Research
A projection method for lp norm location-allocation problems
Mathematical Programming: Series A and B
The Cluster Dissection and Analysis Theory FORTRAN Programs Examples
The Cluster Dissection and Analysis Theory FORTRAN Programs Examples
Clustering Algorithms
Heuristic solution of the multisource Weber problem as a p-median problem
Operations Research Letters
A Hybrid Heuristic for the p-Median Problem
Journal of Heuristics
Cooperative Parallel Variable Neighborhood Search for the p-Median
Journal of Heuristics
Solution approaches for facility location of medical supplies for large-scale emergencies
Computers and Industrial Engineering
Categorical data fuzzy clustering: An analysis of local search heuristics
Computers and Operations Research
A memetic genetic algorithm for the vertex p-center problem
Evolutionary Computation
Region-rejection based heuristics for the capacitated multi-source Weber problem
Computers and Operations Research
e-Work based collaborative optimization approach for strategic logistic network design problem
Computers and Industrial Engineering
A guided reactive GRASP for the capacitated multi-source Weber problem
Computers and Operations Research
Single-Source Capacitated Multi-Facility Weber Problem-An iterative two phase heuristic algorithm
Computers and Operations Research
An aggregation heuristic for large scale p-median problem
Computers and Operations Research
A mixed integer linear model for clustering with variable selection
Computers and Operations Research
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This article presents new heuristic methods for solving a class of hard centroid clustering problems including the p-median, the sum-of-squares clustering and the multi-source Weber problems. Centroid clustering is to partition a set of entities into a given number of subsets and to find the location of a centre for each subset in such a way that a dissimilarity measure between the entities and the centres is minimized. The first method proposed is a candidate list search that produces good solutions in a short amount of time if the number of centres in the problem is not too large. The second method is a general local optimization approach that finds very good solutions. The third method is designed for problems with a large number of centres; it decomposes the problem into subproblems that are solved independently. Numerical results show that these methods are efficient—dozens of best solutions known to problem instances of the literature have been improved—and fast, handling problem instances with more than 85,000 entities and 15,000 centres—much larger than those solved in the literature. The expected complexity of these new procedures is discussed and shown to be comparable to that of an existing method which is known to be very fast.