A minimum expected response model: Formulation, heuristic solution, and application

Rajagopalan, Hari; Saydam, Cem

doi:10.1016/j.seps.2008.12.003

Cited by 43 publications

(18 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rajagopalan and Saydam [58] formulated minimum expected response location problem (MERLP) to determine the locations of ambulances in order to minimize the expected response distances and meet minimum coverage requirements. They incorporated the concept of coverage in their model by using the Daskin's expected coverage [24] as presented in Section 2-1 and the Marianov and Revelle's available coverage [59], in which only those customers are incorporated in the coverage statistics whom covered with a pre-determined reliability.…”

Section: Single Dispatch Total Backup and Non-homogeneous Serversmentioning

confidence: 99%

Hypercube Queuing Models in Emergency Service Systems: A State-of-the-Art Review

2018

View full text Add to dashboard Cite

Abstract. This study provides a review of hypercube queuing models (HQMs) in emergency service systems (ESSs). This survey presents a comprehensive review and taxonomy of models, solutions and applications related to the HQM after Larson [12]. In addition, the structural aspects of HQMs are examined. Important contributions of the existing research are addressed by taking into account multiple factors. In particular, the integration of location decisions with HQMs for designing an ESS is discussed. Finally, a list of issues for future studies are presented.

show abstract

Section: Single Dispatch Total Backup and Non-homogeneous Serversmentioning

confidence: 99%

Hypercube Queuing Models in Emergency Service Systems: A State-of-the-Art Review

2018

View full text Add to dashboard Cite

show abstract

“…Since these models did not consider the probability that an ambulance might be busy at a given time, they were classified as deterministic (Rajagopalan and Saydam [9]). The earliest EMS models have been introduced in the 70s by the articles of Toregas et al [10] and Church and ReVelle [11].…”

Section: Ambulance Location (Deterministic Models)mentioning

confidence: 99%

“…Deterministic models did not account for the probability of a particular ambulance being busy at a given time and overestimated the actual coverage provided [9]. Hence, to compensate for this shortcoming, probabilistic models were later developed.…”

Section: Ambulance Location (Probabilistic Models)mentioning

confidence: 99%

Optimization for the Locations of Ambulances under Two‐Stage Life Rescue in the Emergency Medical Service: A Case Study in Shanghai, China

Liu

Yang

Hao

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

With the development of society, public resources for healthcare are increasingly inadequate to meet the demands for the services. Therefore, it is extremely important for policymakers to provide citizens with the most effective healthcare services within the limited available resources. In order to achieve positive effect rescue operations in the Emergency Medical Services (EMS) system, the problems including where to locate the ambulance facilities and how many ambulance vehicles should be allocated to the stations have become the focus of attention. In this paper, we study the problem based on the demand for EMS in Songjiang District, Shanghai, China, followed by the joint planning of Emergency Medical Services management, which typically consists of ambulance facility locations planning and patient's assignment to hospitals. We proposed a modified Double Standard Model (DSM) to maximize the demand points covered at least two times within the minimum coverage criteria. The problem is solved by integer linear programming technique with the CPLEX software and we make a comparison between the solutions and the locations which exist in the emergency system used by the Songjiang emergency center. Our results show that the demand coverage rate and response time can be efficiently improved through relocating the current facilities without additional vehicle resources.

show abstract

“…The Minimum Expected Response Location Problem (MERLP) minimizes the expected response time while maintaining coverage requirements (Rajagopalan and Saydam 2009). Minimizing expected response times saves lives, prevents permanent injuries and reduces suffering.…”

Section: Homogeneous Lanchester Equationsmentioning

confidence: 99%

Half Space

2013

Encyclopedia of Operations Research and Management Science

View full text Add to dashboard Cite

A minimum expected response model: Formulation, heuristic solution, and application

Cited by 43 publications

References 35 publications

Hypercube Queuing Models in Emergency Service Systems: A State-of-the-Art Review

Hypercube Queuing Models in Emergency Service Systems: A State-of-the-Art Review

Optimization for the Locations of Ambulances under Two‐Stage Life Rescue in the Emergency Medical Service: A Case Study in Shanghai, China

Half Space

Contact Info

Product

Resources

About