An adaptive multiphase approach for large unconditional and conditional p-median problems

Irawan, Chandra A., Salhi, Said, Scaparra, Maria Paola (2014) An adaptive multiphase approach for large unconditional and conditional p-median problems. European Journal of Operational Research, 237 (2). pp. 590-605. ISSN 0377-2217. (doi:10.1016/j.ejor.2014.01.050)

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http://dx.doi.org/10.1016/j.ejor.2014.01.050

Abstract

A multiphase approach that incorporates demand points aggregation, Variable Neighbourhood Search (VNS) and an exact method is proposed for the solution of large-scale unconditional and conditional p-median problems. The method consists of four phases. In the first phase several aggregated problems are solved with a "Local Search with Shaking" procedure to generate promising facility sites which are then used to solve a reduced problem in Phase 2 using VNS or an exact method. The new solution is then fed into an iterative learning process which tackles the aggregated problem (Phase 3). Phase 4 is a post optimisation phase applied to the original (disaggregated) problem. For the p-median problem, the method is tested on three types of datasets which consist of up to 89,600 demand points. The first two datasets are the BIRCH and the TSP datasets whereas the third is our newly geometrically constructed dataset that has guaranteed optimal solutions. The computational experiments show that the proposed approach produces very competitive results. The proposed approach is also adapted to cater for the conditional p-median problem with interesting results.

Item Type: Article
DOI/Identification number: 10.1016/j.ejor.2014.01.050
Subjects: Q Science > Operations Research - Theory
Divisions: Faculties > Social Sciences > Kent Business School > Management Science
Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User: Said Salhi
Date Deposited: 20 Mar 2014 13:04 UTC
Last Modified: 20 Jun 2019 11:16 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/38849 (The current URI for this page, for reference purposes)
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