Benchmarking online dispatch algorithms for Emergency Medical Services

Jagtenberg, Caroline; Berg, Pieter L. van den; Mei, R.D. van der

doi:10.1016/j.ejor.2016.08.061

Cited by 33 publications

(13 citation statements)

References 16 publications

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“…The interaction between scheduling, real-world implementation, and uncertain transportation time confirmation is modeled by a simulative experimental environment proposed in this paper. The mechanics of the information update are the major difference between the experiments in this paper and other existing studies [4], [14], [24], [25].…”

Section: Simulative Experimental Environmentsmentioning

confidence: 86%

“…State estimation and prediction mechanics were applied in each horizon to mitigate the drawbacks of a lack of information. Jagtenberg et al [25] has benchmarked the problem of ambulance dispatching in the scenario of continuously arriving emergency calls, and an offline model in which all the events that may occur in the future are known in advance was utilized as the lower bound of scheduling performance. In all the above studies, decisions are made within the IG procedure, and schedule fixing is also investigated.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, the demand amount of these affected areas is estimated according to the number of people serviced at each location. The urgency degree and corresponding moment are generated randomly within the range of [1,11] and [5,25] and both obey a uniform distribution. The amount of storage at each warehouse is set to 99999 units.…”

Section: A Test Instancesmentioning

confidence: 99%

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Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Zhang¹,

Liu²

2021

IEEE Access

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In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period postdisaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a realworld instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.INDEX TERMS Emergency service, uncertain environment, humanitarian logistics, Pareto optimization, robustness, multiphase scheduling.

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Section: Simulative Experimental Environmentsmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

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Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Zhang¹,

Liu²

2021

IEEE Access

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“…The simulation model uses an agent-based model controlled by a city agent that takes the role of the emergency medical service. It allocates and dispatches vehicles using a closest dispatching rule [45,46,47]. A network agent simulates traffic stochastic conditions and EMS vehicle movements by using nodes and arcs as an abstraction of the reality, pre-computing travel times for different periods of the day and the week using Google's Directions API.…”

Section: Ems Simulatormentioning

confidence: 99%

Active learning metamodeling for policy analysis: Application to an emergency medical service simulator

Antunes

Amorim

Pereira

et al. 2019

Simulation Modelling Practice and Theory

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Simulation approaches constitute a well-established tool to model, understand and predict the behavior of transportation systems, and ultimately to assess the performance of transportation policies. Due to their multidimensionality nature, such systems are not often approachable through conventional analytic methods, making simulation modeling the only reliable tool of study. Nevertheless, simulation models can turn out to be computationally expensive when embedded with enough detail. An immediate answer to this shortcoming is the use of simulation metamodels that are designed to approximate the simulators' results.In this work, the authors propose a metamodeling approach based on active learning that seeks to improve the exploration process of the simulation input space and the associated output behavior. A Gaussian Process (GP) is used as a metamodel to approximate the function inherently defined by the simulation model itself. The GPs are able to nicely handle the uncertainty associated with their predictions, which eventually can be improved with active learning through simulation runs. This provides a practical and efficient way to analyze the simulator's behavior and therefore to assess the performance of policies regarding the underlying real-world systems and services.The authors illustrate the proposed methodology using an Emergency Medical Service (EMS) simulator. Two outputs are analyzed and compared, namely, the survival rate and response time averages. The medical emergency response time recommendation of eight minutes is explored as well its relation with the survival rate. The results show that this methodology is able to identify regions in the simulation input space that might affect the performance and success of medical policies with regards to emergency vehicle dispatching services.

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“…At the present time, however, vehicle dispatching rules for medical emergency requests follow distance- or time-based metrics such as the classic closest idle vehicle dispatching rule ( 12 , 26 , 27 ), which consist of allocating the vehicle that is closest to the emergency occurrence site. This rule is tied to the previously mentioned classic performance metrics that focus on the overall system response time.…”

Section: Literature Reviewmentioning

confidence: 99%

Emergency Medical Service Response: Analyzing Vehicle Dispatching Rules

Amorim

Ferreira

Couto

2018

Transportation Research Record: Journal of the Transportation R

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In an era of information and advanced computing power, emergency medical services (EMS) still rely on rudimentary vehicle dispatching and reallocation rules. In many countries, road conditions such as traffic or road blocks, exact vehicle positions, and demand prediction are valuable information that is not considered when locating and dispatching emergency vehicles. Within this context, this paper presents an investigation of different EMS vehicle dispatching rules by comparing them using various metrics and frameworks. An intelligent dispatching algorithm is proposed, and survival metrics are introduced to compare the new concepts with the classic ones. This work shows that the closest idle vehicle rule (classic dispatching rule) is far from optimal and even a random dispatching of vehicles can outperform it. The proposed intelligent algorithm has the best performance in all the tested situations where resources are adequate. If resources are scarce, especially during peaks in demand, dispatching delays will occur, degrading the system’s performance. In this case, no conclusion could be drawn as to which rule might be the best option. Nevertheless, it draws attention to the need for research focused on managing dispatch delays by prioritizing the waiting calls that inflict the higher penalty on the system performance. Finally, the authors conclude that the use of real traffic information introduces a considerable gain to the EMS response performance.

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Benchmarking online dispatch algorithms for Emergency Medical Services

Cited by 33 publications

References 16 publications

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Active learning metamodeling for policy analysis: Application to an emergency medical service simulator

Emergency Medical Service Response: Analyzing Vehicle Dispatching Rules

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