Optimal Dispatch in Emergency Service System via Reinforcement Learning

PurposeRefugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.Design/methodology/approachA spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.FindingsThe derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.Research limitations/implicationsSome parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.Practical implicationsThe model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.Originality/valueThe study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.

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Modeling the effectiveness and cost-effectiveness of an emergency service system

Social Science & Medicine Part C Medical Economics ◽

10.1016/0160-7995(81)90004-6 ◽

1981 ◽

Vol 15 (1) ◽

pp. 13-17 ◽

Cited By ~ 2

Author(s):

Alfred Hallstrom ◽

Mickey S. Eisenberg ◽

Lawrence Bergner

Keyword(s):

Cost Effectiveness ◽

Emergency Service ◽

Service System

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Emergency service system for urban population: problems and directions of perfection

Bulletin of Siberian Medicine ◽

10.20538/1682-0363-2003-4-79-87 ◽

2003 ◽

Vol 2 (4) ◽

pp. 79-87

Author(s):

N. G. Zenkin

Keyword(s):

Public Health ◽

Russian Federation ◽

Urban Population ◽

Emergency Service ◽

Service System ◽

Important Task ◽

Service Development ◽

Legal Foundation ◽

Public Health Protection ◽

Regional Programs

Perfection of emergency service is one of the most important task of the structure reforming of public health protection. Currently, the main tasks are: development and application of economic mechanisms of the primary and acute medical aid control; preparation and approval of the regulation of emergency service and organization of its activity in routine and in extraordinary situations; formation of standard methodical legal foundation for optimization of the functioning of institutions, departments and emergency specialists; development of recommended models of regional programs of emergency service development and reorganization in regions of Russian Federation.

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FAIR FACILITY ALLOCATION IN EMERGENCY SERVICE SYSTEM

Journal of Business Economics and Management ◽

10.3846/jbem.2020.12823 ◽

2020 ◽

Vol 21 (4) ◽

pp. 1058-1071

Author(s):

Jaroslav Janáček ◽

Lýdia Gábrišová ◽

Miroslav Plevný

Keyword(s):

Emergency Service ◽

Service System ◽

Allocation Problem ◽

Dynamic Programming Principle ◽

Objective Functions ◽

The Family ◽

Finite Set ◽

Problem Instances ◽

Original Approach ◽

Average User

The request of equal accessibility must be respected to some extent when dealing with problems of designing or rebuilding of emergency service systems. Not only the disutility of the average user but also the disutility of the worst situated user must be taken into consideration. Respecting this principle is called fairness of system design. Unfairness can be mitigated to a certain extent by an appropriate fair allocation of additional facilities among the centres. In this article, two criteria of collective fairness are defined in the connection with the facility allocation problem. To solve the problems, we suggest a series of fast algorithms for solving of the allocation problem. This article extends the family of the original solving techniques based on branch-and-bound principle by newly suggested techniques, which exploit either dynamic programming principle or convexity and monotony of decreasing nonlinearities in objective functions. The resulting algorithms were tested and compared performing numerical experiments with real-sized problem instances. The new proposed algorithms outperform the original approach. The suggested methods are able to solve general min-sum and min-max problems, in which a limited number of facilities should be assigned to individual members from a finite set of providers.

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The operation of an Emergency Medical Department in a county hospital from a logistic point of view

Analecta Technica Szegedinensia ◽

10.14232/analecta.2014.2.109-113 ◽

2014 ◽

Vol 8 (2) ◽

pp. 109-113

Author(s):

Gábor Markó ◽

József Gál

Keyword(s):

Emergency Department ◽

Emergency Service ◽

Service System ◽

The United States ◽

Point Of View ◽

Medical Department ◽

Emergency Medical ◽

The One ◽

Medical Attendance ◽

The University

The purpose of this article is to give an overview of the actual emergency medical attendance through an exemplary hospital in Hungary, highlighting its possible imperfections which could perhaps be improved through further structural developments. In order to be expressive, the article follows through the journey of two nominal patients who turned up in the emergency department of the hospital. The importance of this topic is expressed by the fitful judgment of the emergency attendance. Emergency service had already existed in the United States, only later then did the one-entrance service system start to develop Hungary. In some places this system has been working well for decades, but for instance at the University of Szeged – due to the uncertain judgment of the system – the construction is just being finalized, right at the time when such studies are published that question the reason of existence of the emergency departments – at least in their actual form.

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Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning

ACM Transactions on Cyber-Physical Systems ◽

10.1145/3447623 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-24

Author(s):

Jianguo Chen ◽

Kenli Li ◽

Keqin Li ◽

Philip S. Yu ◽

Zeng Zeng

Keyword(s):

Reinforcement Learning ◽

Dynamic Balance ◽

Supply And Demand ◽

Pareto Frontier ◽

Cyber Physical Systems ◽

Physical Systems ◽

Optimal Dispatch ◽

Multi Objective ◽

Dispatching Algorithm ◽

Multiple Dispatch

As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this article, we propose MORL-BD, a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of cyber-physical systems and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent and multi-objective reinforcement learning model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally get the Pareto frontier. Experimental results on the actual DL-PBS show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.

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