Joint relocation and districting using a nested compliance model for EMS systems

Sudtachat, Kanchala; Mayorga, Marı́a E.; Chanta, Sunarin; Albert, Laura A.

doi:10.1016/j.cie.2020.106327

Cited by 15 publications

(4 citation statements)

References 47 publications

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“…Gendreau et al formulate MEXCRP, for which the solution was precomputed at the beginning of the planning period, to provide adequate demand coverage while regulating the number of relocations (11). Maleki (30).…”

Section: Related Literaturementioning

confidence: 99%

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Repositioning Game for Ambulance Services

Rudramoorthi

Amit

2023

Transportation Research Record: Journal of the Transportation R

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In emergency medical services, ambulances are the first line of response, and the location of ambulances is one of the critical factors that influence the response time and, in turn, the coverage that is used as a metric to quantify the performance of an ambulance network. In this paper, we contribute to the literature of the dynamic repositioning of ambulances using compliance tables to improve coverage. We use a centrality measure, the Shapley value—a solution concept from coalitional game theory that measures the importance of a node in a network—to develop a compliance table for ambulance repositioning. Since the problem of computing the Shapley value often comes under the complexity class of [Formula: see text], we propose a compact representation of the ambulance repositioning problem called the repositioning game. The repositioning game includes the unavailability of ambulances and the variability in travel times. We show that the Shapley value of the repositioning game can be computed in polynomial [Formula: see text] time. Using the ambulance demand data of Chennai, India, we conduct discrete event simulation, and the simulation results indicate that the Shapley-value-based compliance table provides higher coverage than the criticality-based compliance table and the compliance table developed using the maximum expected covering location problem (MEXCRP) model when the ambulance network is constrained.

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Section: Related Literaturementioning

confidence: 99%

“…Van Barneveld et al modify the hypercube model to estimate the busy fractions and extend MEXCRP by considering two types of ambulances to develop nested compliance tables ( 29 ). Sudtachat et al develop the maximum realized covering relocation and districting problem and determine the relocation strategies in each district ( 30 ).…”

Section: Related Literaturementioning

confidence: 99%

Repositioning Game for Ambulance Services

Rudramoorthi

Amit

2023

Transportation Research Record: Journal of the Transportation R

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“…However, current computational technology, information technology, and geographic information systems motivate a posteriori type tools development. Several authors (Yue et al [134], Majzoubi et al [135], Mason [83], Billhardt et al [101], Jagtenberg et al [65], Lam et al [105], Van Barneveld, [106,109]; Enayati et al [110], Sudtachat et al [67], Yoon and Albert [136], Roa et al [75], Sun et al [137], Yuangyai et al [138], Nilsang et al [139], van Buuren et al [140], Tsai et al [89]) worked on real-time response tools, which, in the current context, are selfevident as a promising research development line in the future of these tools.…”

Section: Modeling Issuesmentioning

confidence: 99%

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado

Escobar

McClean³

2022

IJGI

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In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

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“…The primary aim of sectorization is commonly defined as dividing a large region into smaller and balancing sectors; however, it encompasses a broader range of implications that vary depending on the context [ 44 ]. It has several applications in political districting [ 25 ], supply chain network design [ 48 ], elderly care service districting [ 24 ], emergency medical service districting [ 34 ], power distribution networks [ 6 ], police districting [ 23 ], water distribution network design [ 46 ], commercial districting [ 29 ], and school districting [ 11 ]. As demonstrated by these examples, sectorization is closely related to the concept of districting, but it also intersects with balancing [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic resectorization to improve utility of healthcare systems

Teymourifar,

Trindade

2024

J Health Popul Nutr

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Balancing is an essential challenge in healthcare systems that requires effective strategies. This study aims to address this crucial issue by suggesting a practical approach. We show the potential of balancing a regional healthcare system to improve its utility. We consider a regional healthcare system comprising multiple hospitals with different sizes, capacities, quality of service, and accessibility. We define a utility function for the system based on the sectorization concept, which endeavors to form a balance between hospitals in terms of essential outputs such as waiting times and demands. The dynamic nature of the system means that this balance degrades over time, necessitating periodic sectorization, which is called resectorization. Our methodology stands out for incorporating resectorization as a dynamic strategy, enabling more flexible and responsive adaptations to continuously changing healthcare needs. Unlike previous studies, based on a system-oriented approach, our resectorization scenarios include the periodic closure of some hospitals. This enables us to enhance both the capacity and quality of healthcare facilities. Furthermore, in contrast to other studies, we investigate the states of diminishing demand throughout the resectorization process. To provide empirical insights, we conduct a simulation using data from a real-world case study. Our analysis spans multiple time periods, enabling us to dynamically quantify the utility of the healthcare system. The numerical findings demonstrate that substantial utility improvements are attainable through the defined scenarios. The study suggests a practical solution to the critical challenge of balancing issues in regional healthcare systems.

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Joint relocation and districting using a nested compliance model for EMS systems

Cited by 15 publications

References 47 publications

Repositioning Game for Ambulance Services

Repositioning Game for Ambulance Services

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Dynamic resectorization to improve utility of healthcare systems

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