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A Genetic Algorithm Based Approach for the Uncapacitated Continuous Location–Allocation Problem.

Salhi, Said, Gamal, M.D.H. (2003) A Genetic Algorithm Based Approach for the Uncapacitated Continuous Location–Allocation Problem. Annals of Operations Research, 123 (1-4). pp. 203-222. ISSN 0254-5330. (doi:10.1023/A:1026131531250) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:5234)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1023/A:1026131531250

Abstract

A GA-based approach is introduced to address the continuous location–allocation problem. Selection and removal procedures based on groups of chromosomes instead of individual chromosomes are put forward and specific crossover and mutation operators that rely on the impact of the genes are proposed. A new operator that injects once in a while new chromosomes into the population is also introduced. This provides diversity within the search and attempts to avoid early convergence. This approach is tested on existing data sets using several runs to evaluate the robustness of the proposed GA approach.

Item Type: Article
DOI/Identification number: 10.1023/A:1026131531250
Uncontrolled keywords: GA heuristic, location, continuous space
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Said Salhi
Date Deposited: 05 Sep 2008 17:10 UTC
Last Modified: 19 Sep 2023 15:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/5234 (The current URI for this page, for reference purposes)

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