Location allocation modeling for healthcare facility planning in Malaysia

Malaysia has seen tremendous growth in the standard of living and household per capita income. The demand for a more systematic and efficient planning has become increasingly more important, one of the keys to achieving a high standard in healthcare. In this paper, a Maximal Covering Location Proble...

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出版年:Computers and Industrial Engineering
第一著者: 2-s2.0-84858698442
フォーマット: 論文
言語:English
出版事項: 2012
オンライン・アクセス:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858698442&doi=10.1016%2fj.cie.2011.12.026&partnerID=40&md5=511da605a86ae35ea7ff9ce94b14ac33
id Shariff S.S.R.; Moin N.H.; Omar M.
spelling Shariff S.S.R.; Moin N.H.; Omar M.
2-s2.0-84858698442
Location allocation modeling for healthcare facility planning in Malaysia
2012
Computers and Industrial Engineering
62
4
10.1016/j.cie.2011.12.026
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858698442&doi=10.1016%2fj.cie.2011.12.026&partnerID=40&md5=511da605a86ae35ea7ff9ce94b14ac33
Malaysia has seen tremendous growth in the standard of living and household per capita income. The demand for a more systematic and efficient planning has become increasingly more important, one of the keys to achieving a high standard in healthcare. In this paper, a Maximal Covering Location Problem (MCLP) is used to study the healthcare facilities of one of the districts in Malaysia. We address the limited capacity of the facilities and the problem is formulated as Capacitated MCLP (CMCLP). We propose a new solution approach based on genetic algorithm to examine the percentage of coverage of the existing facilities within the allowable distance specified/targeted by Malaysian government. The algorithm was shown to generate good results when compared to results obtained using CPLEX version 12.2 on a medium size problem consisting of 179 nodes network. The algorithm was extended to solve larger network consisting of 809 nodes where CPLEX failed to produce non-trivial solutions. We show that the proposed solution approach produces significant results in determining good locations for the facility such that the population coverage is maximized. © 2011 Elsevier Ltd. All rights reserved.

3608352
English
Article

author 2-s2.0-84858698442
spellingShingle 2-s2.0-84858698442
Location allocation modeling for healthcare facility planning in Malaysia
author_facet 2-s2.0-84858698442
author_sort 2-s2.0-84858698442
title Location allocation modeling for healthcare facility planning in Malaysia
title_short Location allocation modeling for healthcare facility planning in Malaysia
title_full Location allocation modeling for healthcare facility planning in Malaysia
title_fullStr Location allocation modeling for healthcare facility planning in Malaysia
title_full_unstemmed Location allocation modeling for healthcare facility planning in Malaysia
title_sort Location allocation modeling for healthcare facility planning in Malaysia
publishDate 2012
container_title Computers and Industrial Engineering
container_volume 62
container_issue 4
doi_str_mv 10.1016/j.cie.2011.12.026
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858698442&doi=10.1016%2fj.cie.2011.12.026&partnerID=40&md5=511da605a86ae35ea7ff9ce94b14ac33
description Malaysia has seen tremendous growth in the standard of living and household per capita income. The demand for a more systematic and efficient planning has become increasingly more important, one of the keys to achieving a high standard in healthcare. In this paper, a Maximal Covering Location Problem (MCLP) is used to study the healthcare facilities of one of the districts in Malaysia. We address the limited capacity of the facilities and the problem is formulated as Capacitated MCLP (CMCLP). We propose a new solution approach based on genetic algorithm to examine the percentage of coverage of the existing facilities within the allowable distance specified/targeted by Malaysian government. The algorithm was shown to generate good results when compared to results obtained using CPLEX version 12.2 on a medium size problem consisting of 179 nodes network. The algorithm was extended to solve larger network consisting of 809 nodes where CPLEX failed to produce non-trivial solutions. We show that the proposed solution approach produces significant results in determining good locations for the facility such that the population coverage is maximized. © 2011 Elsevier Ltd. All rights reserved.
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