Ambulance Deployment and Shift Scheduling: An Integrated Approach

Rajagopalan, Hari; Saydam, Cem; Setzler, Hubert H.; Sharer, Elizabeth

doi:10.4236/jssm.2011.41010

Cited by 17 publications

(16 citation statements)

References 39 publications

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“…by 2 mi. grid used in Rajagopalan et al [40] resulting in 168 nodes. Further, since calls for EMS services are well documented to vary by time of the day and day of the week, we adopted the same 2-hour time intervals in [40] resulting in 12× 7 = 84 problem instances.…”

Section: The Simulation Modelmentioning

confidence: 99%

“…grid used in Rajagopalan et al [40] resulting in 168 nodes. Further, since calls for EMS services are well documented to vary by time of the day and day of the week, we adopted the same 2-hour time intervals in [40] resulting in 12× 7 = 84 problem instances. Hence, we created 84 call data sets from real data where the time stamp, call priority, service time, and zone the call originated are directly copied from the original call data.…”

Section: The Simulation Modelmentioning

confidence: 99%

“…It is important to note that in our approach the fleet size is an input variable which clearly impacts the average workload of the deployed ambulances where low fleet sizes will result in high workloads and vice versa. Since we are using the same data set used by Rajagopalan et al [40] initially we adapted the fleet sizes prescribed (recommended) by their dynamic expected coverage location (DECL) model which finds the minimum of ambulances to meet the target coverage rate. We noticed that with the DECL prescribed fleet sizes for the 84 problems instances, when we run the simulation optimization model with defaults dispatch policy, the resulting average ambulance workload is about 54-56% which is considered high in the EMS community.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The dynamic available coverage location (DECL) model proposed by Rajagopalan, Saydam, et al [40] determines the minimum number of ambulances and their locations to meet a system wide coverage requirement for each time interval. The authors utilize Jarvis' hypercube approximation algorithm [42].…”

Section: Appendixmentioning

confidence: 99%

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Balancing Ambulance Crew Workloads via a Tiered Dispatch Policy

Saydam

2016

Pesqui. Oper.

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ABSTRACT. Emergency Medical Services (EMS) system's mission is to provide timely and effective treatment to anyone in need of urgent medical care throughout their jurisdiction. The default dispatch policy is to send the nearest ambulance to all medical emergencies and it is widely accepted by many EMS providers. However, sending nearest ambulance is not always optimal, often imposes heavy workloads on ambulance crews posted in high demand zones while reducing available coverage or requiring ambulance relocations to ensure high demand zones are covered adequately. In this paper we propose a tiered dispatch policy to balance the ambulance crew workloads while meeting fast response times for priority 1 calls. We use a tabu search algorithm to determine the initial ambulance locations and a simulation model to evaluate the impact of a tiered dispatch policy on ambulance crew workloads, coverage rates for priority 1-3 calls, and on survivability rate for out of hospital cardiac arrests. We present computational statistics and demonstrate the efficacy of the tiered dispatch policy using real-world data.

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Section: The Simulation Modelmentioning

confidence: 99%

Section: The Simulation Modelmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

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Balancing Ambulance Crew Workloads via a Tiered Dispatch Policy

Saydam

2016

Pesqui. Oper.

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“…That is the faster the ambulance reach the incident the better the chances of survival. Many optimization strategies have been presented in [10], [11] and [12]. Therefore reducing the response time and short-term forecasting plays a significant role in managing hospital resources more efficiently.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Patient Inflow Prediction Model for Hospital Resource Management

Srikanth

Arivazhagan

2017

IJSC

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There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

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