An aggregation heuristic for large scale p-median problem

Avella, Pasquale; Boccia, Maurizio; Salerno, Saverio; Vasilyev, Igor

doi:10.1016/j.cor.2011.09.016

Cited by 62 publications

(42 citation statements)

References 27 publications

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“…Similarly k x and k y can be determined as cor [5] cor [10] cor [15] cor [20] cor [16] cor [21] cor [11] cor [6] cor [1] cor [2] cor [7] cor [17] cor [22] cor [12] cor [3] cor [18] cor [23] cor [8] cor [13] cor [4] cor [19] cor [24] cor [9] cor [14] x…”

Section: ) Building Corner Objectsunclassified

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A quadtree-based allocation method for a class of large discrete Euclidean location problems

Salhi

Irawan

2015

Computers & Operations Research

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Section: ) Building Corner Objectsunclassified

“…In this method, the numbering system is slightly different with the original quadtree system. In level 1, there are 4 quads (1-0, 1-1, 1-2, and 1-3) whereas in level 2 there are 16 quads (2-0, ..., [2][3][4][5][6][7][8][9][10][11][12][13][14][15] …”

Section: ) Building Corner Objectsmentioning

confidence: 99%

A quadtree-based allocation method for a class of large discrete Euclidean location problems

Salhi

Irawan

2015

Computers & Operations Research

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“…Further, to represent most regional or national location problems with just 900 candidate nodes is rather limited and implies a high degree of problem simplification together with a loss of information accuracy. Another set of abstract graphs has been used by Avella [4], with the same drawback of uniform distribution of the demand.…”

Section: Introductionmentioning

confidence: 99%

“…Some tests on larger graphs have been carried out by Avella [4] using the Birch set of abstract graphs (with up to 89 000 nodes). Birch graphs have similar to those of Beasley a uniform distribution of the demand but points are grouped into clusters.…”

Section: Introductionmentioning

confidence: 99%

A Computational Comparison of Different Algorithms for Very Large $$p$$-median Problems

Rebreyend¹,

Lemarchand²,

Euler³

2015

Evolutionary Computation in Combinatorial Optimization

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Citation for the original published chapter:Rebreyend, P., Lemarchand, L., Euler, R. (2015) A computational comparison of different algorithms for very large p -median problems.In : Evolutionary Computation in Combinatorial Optimization: 15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015 Abstract. In this paper, we propose a new method for solving large scale p-median problem instances based on real data. We compare different approaches in terms of runtime, memory footprint and quality of solutions obtained. In order to test the different methods on real data, we introduce a new benchmark for the p-median problem based on real Swedish data. Because of the size of the problem addressed, up to 1938 candidate nodes, a number of algorithms, both exact and heuristic, are considered. We also propose an improved hybrid version of a genetic algorithm called impGA. Experiments show that impGA behaves as well as other methods for the standard set of medium-size problems taken from Beasley's benchmark, but produces comparatively good results in terms of quality, runtime and memory footprint on our specific benchmark based on real Swedish data.

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“…Constraints (2) guarantee that each customer i is assigned to exactly one open facility whereas constraint (3) restricts the number of open facilities to be exactly p.…”

Section: Introductionmentioning

confidence: 99%

Hybrid meta-heuristics with VNS and exact methods: application to large unconditional and conditional vertex $$p$$ p -centre problems

2015

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Large-scale unconditional and conditional vertex p-centre problems are solved using two meta-heuristics. One is based on a three-stage approach whereas the other relies on a guided multi-start principle. Both methods incorporate Variable Neighbourhood Search, exact method, and aggregation techniques. The methods are assessed on the TSP dataset which consist of up to 71,009 demand points with p varying from 5 to 100. To the best of our knowledge, these are the largest instances solved for unconditional and conditional vertex pcentre problems. The two proposed meta-heuristics yield competitive results for both classes of problems. KeywordsLarge unconditional and conditional vertex p-centre problems, aggregation, variable neighbourhood search, exact method.

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An aggregation heuristic for large scale p-median problem

Cited by 62 publications

References 27 publications

A quadtree-based allocation method for a class of large discrete Euclidean location problems

A quadtree-based allocation method for a class of large discrete Euclidean location problems

A Computational Comparison of Different Algorithms for Very Large $$p$$-median Problems

Hybrid meta-heuristics with VNS and exact methods: application to large unconditional and conditional vertex $$p$$ p -centre problems

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