The paper conducts an assessment of the impact of varying response time distributions on ambulance deployment plans by integrating forecasting, simulation and optimisation techniques to predefined locations with heterogeneous demand patterns. Bulawayo metropolitan city was used as a case study. The paper proposes use of future demand and allows for simultaneous evaluation of operational performances of deployment plans using multiple performance indicators such as average response time, total duration of a call in system, number of calls in response queue, average queuing time, throughput ratios and ambulance utilisation levels. Increasing the fleet size influences the average response time below a certain threshold value across all the heterogeneous regions. However, when fleet size is increased beyond this threshold value, no significant changes occur in the performance indicators. Fleet size varied inversely to ambulance utilisation levels. As fleet size is gradually increased, utilisation levels also gradually decreased. Due care must be taken to avoid under-utilisation of ambulances during deployment. Under utilisation culminates to human and material equipment idleness and yet the resources available are scarce and should be deployed where needed most. For critical resources such as ambulances in emergency response, increasing the resource did not always translate to better performance. However, directing efforts towards reducing response time (call delay time, chute time, queuing and travel time) results in improvement of service performance and corresponding reduction in number of ambulances required to achieve a desired service level. Performance indicators such as utilisation levels and throughput ratios are imperative in ensuring balanced resource allocation and capacity utilisation which avoids under or over utilisation of scarce and yet critical resources. This has a strong bearing on both human and material resource workloads. The integrated strategy can also be replicated with relative ease to manage other service systems with a server-to-customer relationship.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 14, Issue 1) |
| DOI | 10.11648/j.ajtas.20251401.12 |
| Page(s) | 12-29 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Heterogeneous Regions, Simulation, Optimisation, Performance Indicators, Response Time Distributions, Ambulance Deployment Plan
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APA Style
Mapuwei, T. W., Bodhlyera, O., Mwambi, H. (2025). Impact of Varying Response Time on Ambulance Deployment Plans in Heterogeneous Regions Using Multiple Performance Indicators. American Journal of Theoretical and Applied Statistics, 14(1), 12-29. https://doi.org/10.11648/j.ajtas.20251401.12
ACS Style
Mapuwei, T. W.; Bodhlyera, O.; Mwambi, H. Impact of Varying Response Time on Ambulance Deployment Plans in Heterogeneous Regions Using Multiple Performance Indicators. Am. J. Theor. Appl. Stat. 2025, 14(1), 12-29. doi: 10.11648/j.ajtas.20251401.12
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Download@article{10.11648/j.ajtas.20251401.12,
author = {Tichaona Wilbert Mapuwei and Oliver Bodhlyera and Henry Mwambi},
title = {Impact of Varying Response Time on Ambulance Deployment Plans in Heterogeneous Regions Using Multiple Performance Indicators},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {14},
number = {1},
pages = {12-29},
doi = {10.11648/j.ajtas.20251401.12},
url = {https://doi.org/10.11648/j.ajtas.20251401.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20251401.12},
abstract = {The paper conducts an assessment of the impact of varying response time distributions on ambulance deployment plans by integrating forecasting, simulation and optimisation techniques to predefined locations with heterogeneous demand patterns. Bulawayo metropolitan city was used as a case study. The paper proposes use of future demand and allows for simultaneous evaluation of operational performances of deployment plans using multiple performance indicators such as average response time, total duration of a call in system, number of calls in response queue, average queuing time, throughput ratios and ambulance utilisation levels. Increasing the fleet size influences the average response time below a certain threshold value across all the heterogeneous regions. However, when fleet size is increased beyond this threshold value, no significant changes occur in the performance indicators. Fleet size varied inversely to ambulance utilisation levels. As fleet size is gradually increased, utilisation levels also gradually decreased. Due care must be taken to avoid under-utilisation of ambulances during deployment. Under utilisation culminates to human and material equipment idleness and yet the resources available are scarce and should be deployed where needed most. For critical resources such as ambulances in emergency response, increasing the resource did not always translate to better performance. However, directing efforts towards reducing response time (call delay time, chute time, queuing and travel time) results in improvement of service performance and corresponding reduction in number of ambulances required to achieve a desired service level. Performance indicators such as utilisation levels and throughput ratios are imperative in ensuring balanced resource allocation and capacity utilisation which avoids under or over utilisation of scarce and yet critical resources. This has a strong bearing on both human and material resource workloads. The integrated strategy can also be replicated with relative ease to manage other service systems with a server-to-customer relationship.},
year = {2025}
}
TY - JOUR T1 - Impact of Varying Response Time on Ambulance Deployment Plans in Heterogeneous Regions Using Multiple Performance Indicators AU - Tichaona Wilbert Mapuwei AU - Oliver Bodhlyera AU - Henry Mwambi Y1 - 2025/01/14 PY - 2025 N1 - https://doi.org/10.11648/j.ajtas.20251401.12 DO - 10.11648/j.ajtas.20251401.12 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 12 EP - 29 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20251401.12 AB - The paper conducts an assessment of the impact of varying response time distributions on ambulance deployment plans by integrating forecasting, simulation and optimisation techniques to predefined locations with heterogeneous demand patterns. Bulawayo metropolitan city was used as a case study. The paper proposes use of future demand and allows for simultaneous evaluation of operational performances of deployment plans using multiple performance indicators such as average response time, total duration of a call in system, number of calls in response queue, average queuing time, throughput ratios and ambulance utilisation levels. Increasing the fleet size influences the average response time below a certain threshold value across all the heterogeneous regions. However, when fleet size is increased beyond this threshold value, no significant changes occur in the performance indicators. Fleet size varied inversely to ambulance utilisation levels. As fleet size is gradually increased, utilisation levels also gradually decreased. Due care must be taken to avoid under-utilisation of ambulances during deployment. Under utilisation culminates to human and material equipment idleness and yet the resources available are scarce and should be deployed where needed most. For critical resources such as ambulances in emergency response, increasing the resource did not always translate to better performance. However, directing efforts towards reducing response time (call delay time, chute time, queuing and travel time) results in improvement of service performance and corresponding reduction in number of ambulances required to achieve a desired service level. Performance indicators such as utilisation levels and throughput ratios are imperative in ensuring balanced resource allocation and capacity utilisation which avoids under or over utilisation of scarce and yet critical resources. This has a strong bearing on both human and material resource workloads. The integrated strategy can also be replicated with relative ease to manage other service systems with a server-to-customer relationship. VL - 14 IS - 1 ER -
Department of Statistics and Mathematics, Faculty of Science and Engineering, Bindura University of Science Education, Bindura, Zimbabwe; School of Mathematics, Statistics and Computer Science, University of Kwa Zulu-Natal, Pietermaritzburg, South Africa
School of Mathematics, Statistics and Computer Science, University of Kwa Zulu-Natal, Pietermaritzburg, South Africa
School of Mathematics, Statistics and Computer Science, University of Kwa Zulu-Natal, Pietermaritzburg, South Africa
