A Stochastic Programming Approach for Locating and Dispatching Two Types of Ambulances

Yoon, Soovin; Albert, Laura A.; White, Veronica M.

doi:10.1287/trsc.2020.1023

Cited by 35 publications

(7 citation statements)

References 39 publications

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“…Furthermore, based on information provided by the emergency caller and the dispatch code determined by MDPS, the EMT may decide that the dispatch of a highly equipped ERU staffed with advanced healthcare professionals is not required in some instances (19). Subsequently, they may dispatch a "non-transport unit" as a follow-up unit for patients with minor medical complaints.…”

Section: Dispatcher-initiated Decisions (Did)mentioning

confidence: 99%

Understanding Non-Transport Decision Theories in the Pre-hospital Setting: A Comprehensive Narrative Review

Farhat

Laughton

Aifa

et al. 2023

Preprint

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Background: Pre-hospital emergency calls do not always result in the patient being transported to the hospital. Such decisions can jeopardize healthcare outcomes and lead to wasting resources. Objectives and Methods: The purpose of this review was to summarize the various theories reported in the literature regarding patient non-conveyance to healthcare facilities in the pre-hospital emergency setting. Studies published between January 2012 and August 2022 were identified from PubMed and Google Scholar. These studies were subsequently screened using the AL-Rayyan® software. Results and Discussion: Twenty-nine articles that summarized patient non-transport theories were identified. Patient non-transport is a crucial health issue. The factors related to the non-transport were: patient-initiated refusals (PIR), clinical-initiated decisions (CID), and dispatcher-initiated decisions (DID). Conclusions: Patient non-transport to hospitals remains a serious challenge for emergency care systems. Hence, it requires greater attention from different healthcare systems. Further analysis is necessary to understand pre-hospital non-transport events and identify improvement areas. This approach helps prevent the wastage of resources while ensuring that patients receive the appropriate and definitive care they require.

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Section: Dispatcher-initiated Decisions (Did)mentioning

confidence: 99%

Understanding Non-Transport Decision Theories in the Pre-hospital Setting: A Comprehensive Narrative Review

Farhat

Laughton

Aifa

et al. 2023

Preprint

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“…Similarly, Yoon et al. (2021) have modeled a siting and dispatching problem for multiple types of emergency vehicles. The model aims to maximize the coverage of the demand while prioritizing it.…”

Section: Literature Reviewmentioning

confidence: 99%

Joint column generation and Lagrangian relaxation technique for incident respondent location and allocation

Atik

Hajibabai

2023

Computer aided Civil Eng

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Incident response operations require effective planning of resources to ensure timely clearance of roadways and avoidance of secondary incidents. This study formulates a mixed‐integer linear program to minimize the total expected travel time and maximize the demand covered. The model accounts for the location, severity, frequency of incidents, dispatching locations, and availability of incident respondents. An integrated methodology that includes column generation and Lagrangian relaxation with a density‐based clustering technique that defines incident hot spots is proposed. The hybrid approach is applied to an empirical case study in Raleigh, NC. A network instance with 10,672 incident sites, clustered with a search distance (ε) of 5 min, is solved efficiently with an optimality gap of 1.37% in 2 min. A Benders decomposition technique is implemented to conduct benchmark analyses. The numerical results suggest that the proposed algorithm can solve the problem efficiently and outperform the benchmark solutions.

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“…Yoon et al [73] present a stochastic model for joint location and dispatching of ambulances under demand uncertainty.…”

Section: Marianov and Revellementioning

confidence: 99%

Ambulance location under temporal variation in demand using a mixed coded memetic algorithm

2022

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Emergency medical services (EMS) are among the most important services in any society due to their role in saving people's lives and reducing morbidities. The location of ambulance stations and the allocation of ambulances to the stations is an important planning problem for any EMS system to ensure adequate coverage while minimising the response time. This study considers a mixed-integer programming model that determines the ambulance locations by considering the time of day variations in demand. The presented model also considers heterogeneous performance measures based on survival function and coverage for different patient types with varying levels of urgency. A memetic algorithm based-approach that applies a mixed chromosome representation for solutions is proposed to solve the problem. Our computational results indicate that neglecting time-dependent variation of demand can underestimate the number of ambulances required by up to 15% during peak demand. We also demonstrate the effectiveness of the proposed solution approach in providing good quality solutions within a reasonable time.

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A Stochastic Programming Approach for Locating and Dispatching Two Types of Ambulances

Cited by 35 publications

References 39 publications

Understanding Non-Transport Decision Theories in the Pre-hospital Setting: A Comprehensive Narrative Review

Understanding Non-Transport Decision Theories in the Pre-hospital Setting: A Comprehensive Narrative Review

Joint column generation and Lagrangian relaxation technique for incident respondent location and allocation

Ambulance location under temporal variation in demand using a mixed coded memetic algorithm

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