dc.contributor.author |
Karsten, Carike
|
|
dc.contributor.author |
Bean, WL
|
|
dc.contributor.author |
Van Heerden, Quentin
|
|
dc.date.accessioned |
2024-02-06T11:45:36Z |
|
dc.date.available |
2024-02-06T11:45:36Z |
|
dc.date.issued |
2023-02 |
|
dc.identifier.citation |
Karsten, C., Bean, W. & Van Heerden, Q. 2023. Robust facility location of container clinics: A South African application. <i>International Journal of Mathematical, Engineering and Management Science, 8(1).</i> http://hdl.handle.net/10204/13577 |
en_ZA |
dc.identifier.issn |
2455-7544 |
|
dc.identifier.uri |
DOI:10.33889/IJMEMS.2023.8.1.003
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/13577
|
|
dc.description.abstract |
There is a lack of dynamic facility location models for developing countries that consider the changes in the problem environment over time, such as patient population and population migration. Therefore, this paper focuses on using optimization and goal programming to locate health care facilities in an uncertain environment using multiple possible future urban development senarios. To achieve this, a robust multi-objective facility location model is developed and used to determine locations for container clinic deployment over multiple years in selected communities in South Africa. A synthetic population and urban growth simulation model are used to estimate population density and distribution from 2018 to 2030 for three development senarios. The results from the urban growth simulation model are then used as input into the facility location model to locate facilities whilst considering the three future development scenarios. Results of the model indicate that the robust model can be used to find locations that provide a relatively good solution to all considered development scenarios, providing key role players with quantitative decision support during network design under uncertainty. An accessibility analysis investigates the impact of the prescribed accessibility percentage on model results and a budget analysis evaluates the impact of a case that includes a budget constraint. From these two analyses it is illustrated that the model is sensitive to changes in parameters and that the model can be used by key stakeholders to combine network design and urban development planning for improved decision making. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.ijmems.in/cms/storage/app/public/uploads/volumes/03-IJMEMS-22-0426-8-1-43-59-2023.pdf |
en_US |
dc.relation.uri |
https://www.researchgate.net/publication/367764312_Robust_Facility_Location_of_Container_Clinics_A_South_African_Application |
en_US |
dc.source |
International Journal of Mathematical, Engineering and Management Science, 8(1) |
en_US |
dc.subject |
Population migration |
en_US |
dc.subject |
Patient population |
en_US |
dc.subject |
Facility location models |
en_US |
dc.subject |
Goal programming |
en_US |
dc.subject |
Mobile clinics |
en_US |
dc.subject |
Genetic algorithms |
en_US |
dc.subject |
Multiple objectives |
en_US |
dc.subject |
Optimization |
en_US |
dc.title |
Robust facility location of container clinics: A South African application |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
43-59 |
en_US |
dc.description.cluster |
Smart Places |
en_US |
dc.description.impactarea |
Urban and Regional Dynamics |
en_US |
dc.identifier.apacitation |
Karsten, C., Bean, W., & Van Heerden, Q. (2023). Robust facility location of container clinics: A South African application. <i>International Journal of Mathematical, Engineering and Management Science, 8(1)</i>, http://hdl.handle.net/10204/13577 |
en_ZA |
dc.identifier.chicagocitation |
Karsten, Carike, WL Bean, and Quentin Van Heerden "Robust facility location of container clinics: A South African application." <i>International Journal of Mathematical, Engineering and Management Science, 8(1)</i> (2023) http://hdl.handle.net/10204/13577 |
en_ZA |
dc.identifier.vancouvercitation |
Karsten C, Bean W, Van Heerden Q. Robust facility location of container clinics: A South African application. International Journal of Mathematical, Engineering and Management Science, 8(1). 2023; http://hdl.handle.net/10204/13577. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Karsten, Carike
AU - Bean, WL
AU - Van Heerden, Quentin
AB - There is a lack of dynamic facility location models for developing countries that consider the changes in the problem environment over time, such as patient population and population migration. Therefore, this paper focuses on using optimization and goal programming to locate health care facilities in an uncertain environment using multiple possible future urban development senarios. To achieve this, a robust multi-objective facility location model is developed and used to determine locations for container clinic deployment over multiple years in selected communities in South Africa. A synthetic population and urban growth simulation model are used to estimate population density and distribution from 2018 to 2030 for three development senarios. The results from the urban growth simulation model are then used as input into the facility location model to locate facilities whilst considering the three future development scenarios. Results of the model indicate that the robust model can be used to find locations that provide a relatively good solution to all considered development scenarios, providing key role players with quantitative decision support during network design under uncertainty. An accessibility analysis investigates the impact of the prescribed accessibility percentage on model results and a budget analysis evaluates the impact of a case that includes a budget constraint. From these two analyses it is illustrated that the model is sensitive to changes in parameters and that the model can be used by key stakeholders to combine network design and urban development planning for improved decision making.
DA - 2023-02
DB - ResearchSpace
DP - CSIR
J1 - International Journal of Mathematical, Engineering and Management Science, 8(1)
KW - Population migration
KW - Patient population
KW - Facility location models
KW - Goal programming
KW - Mobile clinics
KW - Genetic algorithms
KW - Multiple objectives
KW - Optimization
LK - https://researchspace.csir.co.za
PY - 2023
SM - 2455-7544
T1 - Robust facility location of container clinics: A South African application
TI - Robust facility location of container clinics: A South African application
UR - http://hdl.handle.net/10204/13577
ER -
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en_ZA |
dc.identifier.worklist |
27377 |
en_US |