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Hybrid meta-heuristics with VNS and exact methods: application to large unconditional and conditional vertex p-centre problems
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 technique...
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Published in: | Journal of heuristics 2016-08, Vol.22 (4), p.507-537 |
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creator | Irawan, Chandra Ade Salhi, Said Drezner, Zvi |
description | 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
p
-centre problems. The two proposed meta-heuristics yield competitive results for both classes of problems. |
doi_str_mv | 10.1007/s10732-014-9277-7 |
format | article |
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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
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p
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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
p
-centre problems. 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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
p
-centre problems. The two proposed meta-heuristics yield competitive results for both classes of problems.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10732-014-9277-7</doi><tpages>31</tpages></addata></record> |
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subjects | Algorithms Artificial Intelligence Calculus of Variations and Optimal Control Optimization Customers Heuristic Management Science Mathematics Mathematics and Statistics Methods Neighborhoods Operations Research Operations Research/Decision Theory |
title | Hybrid meta-heuristics with VNS and exact methods: application to large unconditional and conditional vertex p-centre problems |
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