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Ambulance response time is an important factor in saving lives and is highly linked with the ambulance location problem. The Maximum Expected Covering Location Problem (MEXCLP), introduced by Daskin (1983), is one of the most used ambulance location models that maximize the probability of stratifying demands for emergency medical service (EMS) centers. Due to huge increase in the operational costs of EMS centers, ambulance location models must consider the cost of coverage and the opportunity to use other companies’ private ambulances to answer emergency calls. The paper aims to discuss these issues.

In this paper, the authors propose to extend the MEXCLP to a bi-objective optimization problem where the cost of satisfying emergency calls is minimized.

The proposed model is tested using data retrieved from the Lebanese Red Cross (LRC) in Beirut capital of Lebanon. The reported findings show significant enhancements in the results where the LRC can fully satisfy the perceived demands from all areas in Beirut within 9 min with an affordable cost.

The model is a first attempt to reduce operational costs of EMS centers while constraining the response time to satisfy emergency calls at an acceptable rate.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

This paper addresses a location-routing problem (LRP) under uncertainty for providing emergency medical services (EMS) during disasters, which is formulated using a robust optimization (RO) approach. The objectives consist of minimizing relief time and the total cost including location costs and the cost of route coverage by the vehicles (ambulances and helicopters).

A shuffled frog leaping algorithm (SFLA) is developed to solve the problem and the performance is assessed using both the ε-constraint method and NSGA-II algorithm. For a more accurate validation of the proposed algorithm, the four indicators of dispersion measure (DM), mean ideal distance (MID), space measure (SM), and the number of Pareto solutions (NPS) are used.

The results obtained indicate the efficiency of the proposed algorithm within a proper computation time compared to the CPLEX solver as an exact method.

In this study, the planning horizon is not considered in the model which can affect the value of parameters such as demand. Moreover, the uncertain nature of the other parameters such as traveling time is not incorporated into the model.

The outcomes of this research are helpful for decision-makers for the planning and management of casualty transportation under uncertain environment. The proposed algorithm can obtain acceptable solutions for real-world cases.

A novel robust mixed-integer linear programming (MILP) model is proposed to formulate the problem as a LRP. To solve the problem, two efficient metaheuristic algorithms were developed to determine the optimal values of objectives and decision variables.

The purpose of this paper is to apply agent-based modeling and simulation concepts in evaluating different approaches to solve ambulance-dispatching decision problems under bounded rationality. The paper investigates the effect of over-responding, i.e. dispatching ambulances even for doubtful high-risk patients, on the performance of equity constrained emergency medical services.

Agent-based modeling and simulation was used to evaluate two different dispatching policies: first, a policy based on maximum reward, and second, a policy based on the Markov decision process formulation. Four equity constraints were used: two from the patients’ side and two from the providers’ side.

The Markov decision process formulation, solved using value iteration method, performed better than the maximum reward method in terms of number of patients served. As the equity constraints conflict with each other, at most three equity constraints could be enforced at a time. The study revealed that it is safe to over-respond if there is uncertainty in the risk level of the patients.

Further research is required to understand the implications of under-responding, where doubtful high-risk patients are denied an ambulance service.

The need for good triage system is apparent as over-responding badly affects the operational budget. The model can be used for evaluating various dispatching policy decisions.

Emergency medical services have to ensure efficient and equitable provision of services, from the perception of both patients and service providers.

The paper applies agent-based modeling to equity constrained emergency medical services and highlights findings that are not reported in the existing literature.

Like many operations research models, ambulance location models suffer from lack of practical implementation by those who could benefit from them. A major obstacle to adoption of such models is the emphasis their developers place on abstract mathematical principles, which overshadow the functional purpose of the models. This focus leads to impracticality in the presentation and usage of models, owing to such attributes as complex computer interfaces, cumbersome input and output procedures, and non‐intuitive presentation of results. Suggests a solution to these problems, in the form of a graphical interface system called ALIAS (Ambulance Location Identification and Analysis System).

The lack of studies about the response time of emergency medical service during the coronavirus disease 2019 (COVID-19) pandemic in a dense city of a developing country has triggered this study to explore the factors contributing to a high response time of ambulance service to reach patients in need. An evaluation of contributing factors to the response time is necessary to guide decision-makers in keeping a high service level of emergency medical service.

This research employed an agent-based modeling approach with input parameters from interviews with emergency medical service staff in Bandung city, Indonesia. The agent-based model is established to evaluate the relevant contribution of the factors to response time reduction using several scenarios.

According to agent-based simulation, four factors contribute to the response time: the process of preparing crew and ambulance during the pandemic, coverage area, traffic density and crew responsiveness. Among these factors, the preparation process during the pandemic and coverage area significantly contributed to the response time, while the traffic density and crew responsiveness were less significant. The preparation process is closely related to the safety procedure in handling patients during the COVID-19 pandemic and normal time. The recommended coverage area for maintaining a low response time is 5 km, equivalent to six local subdistricts.

This study has explored the factors contributing to emergency medical response time. The insignificant contribution of the traffic density showed that citizens, in general, have high awareness and compliance to traffic priority regulation, so crew responsiveness in handling ambulances is an irrelevant factor. This study might have different contributing factors for less dense population areas and focuses on public emergency medical services provided by the local government.

The local government must provide additional funding to cover additional investment for ambulance, crew and administration for the new emergency service deployment point. Exercising an efficient process in ambulance and crew preparation is mandatory for each emergency deployment point.

This study evaluates the contributing factors of emergency medical response time in the pandemic and normal situation by qualitative analysis and agent-based simulation. The performance comparison in terms of medical response time before and after COVID-19 through agent-based simulation is valuable for decision-makers to reduce the impact of COVID-19.

In emergency services, maximizing population coverage with the lowest cost at the peak of the demand is important. In addition, due to the nature of services in emergency centers, including hospitals, the number of servers and beds is actually considered as the capacity of the system. Hence, the purpose of this paper is to propose a multi-objective maximal covering facility location model for emergency service centers within an M _(t)/M/m/m queuing system considering different levels of service and periodic demand rate.

The process of serving patients is modeled according to queuing theory and mathematical programming. To cope with multi-objectiveness of the proposed model, an augmented ε-constraint method has been used within GAMS software. Since the computational time ascends exponentially as the problem size increases, the GAMS software is not able to solve large-scale problems. Thus, a NSGA-II algorithm has been proposed to solve this category of problems and results have been compared with GAMS through random generated sample problems. In addition, the applicability of the proposed model in real situations has been examined within a case study in Iran.

Results obtained from the random generated sample problems illustrated while both the GAMS software and NSGA-II almost share the same quality of solution, the CPU execution time of the proposed NSGA-II algorithm is lower than GAMS significantly. Furthermore, the results of solving the model for case study approve that the model is able to determine the location of the required facilities and allocate demand areas to them appropriately.

In the most of previous works on emergency services, maximal coverage with the minimum cost were the main objectives. Hereby, it seems that minimizing the number of waiting patients for receiving services have been neglected. To the best of the authors’ knowledge, it is the first time that a maximal covering problem is formulated within an M _(t)/M/m/m queuing system. This novel formulation will lead to more satisfaction for injured people by minimizing the average number of injured people who are waiting in the queue for receiving services.

The purpose of this paper is to investigate the way technology affects the provision of prehospital emergency care, upgrading the quality of services offered and significantly reducing the risk of premature termination of the patients.

The paper presents the development of the eEKAB, a pilot emergency medical information system that simulates the main services offered by the Greek National Instant Aid Centre (EKAB). The eEKAB was developed on an agile system methodology. From a technical perspective, the features and the technology were mainly chosen to provide reliable and user-friendly interfaces that will attract many users. eEKAB is based on three important pillars for offering health care to the patients: the “On-time Incident Reporting”, the “On-time Arrival at the Incident” and “Transfer to the Health Center”. According to the literature review, the emergency medical services (EMS) systems that combine all the features are very few.

It reduces the total time of the EMS procedures and it allows for an easier management of EMS, by providing a better allocation of human resources and a better geographical distribution of ambulances. The evaluation displayed that it is a very helpful application for the ambulance drivers as it reduces the ambulance response time to arrive in the patient's location and contributes significantly to the general performance of the prehospital medical care system. Also, the survey verified the importance of implementing eEKAB on a larger scale beyond the pilot usage. It is worth mentioning that the younger ambulance drivers had a more positive view for the purpose of the application.

The paper clearly identifies implications for further research. Regarding interoperability, the mobile app cooperates with the Operational Center of EKAB, while further collaboration could be achieved with other operational ambulance handling center, mainly, of the private sector. The system can evolve to include better communications among the EKAB departments. Particularly, the ambulance crew as well as the doctors should be informed with more incident features such as the emergency signal so that they know whether to open the siren, the patient's name, etc. The authors are currently working on implementing some features to provide effective medical health services to the patient in the ambulance.

eEKAB will have very significant implications in case of its enforcement, such as the reduction of the total time of EMS procedures with a corresponding reduction of the operating costs of an accident management system and an ambulance fleet handling system while in parallel informing in time the doctors/clinics. It will provide better distribution of ambulances as well as of total human resources. It will greatly assist ambulance drivers, while reducing ambulance response time to reach the patient's location. In other words, the authors will have a better performance of the whole prehospital care system.

Providing emergency care before the hospital is of great importance for upgrading the quality of health services provided at the accident site, thus significantly reducing the risk of premature death of patients. This in itself has a significant social implication.

The paper demonstrates a solid understanding in the field of the EMS systems and the corresponding medical services offered. It proposes the development of an effective, feasible and innovative EMS information system that will improve the existing emergency health care system in Greece (EKAB). An in depth literature review and presentation of the adopted new technologies and the respective architecture take place. An evaluation and statistical validation were conducted for proving the high applicability of eEKAB in case of real-life running.

The purpose of this paper is to statistically assess the effects of the design factors including usage of data-driven decision support tool (DST), classification of patients (triage), prioritization based on vital scores of patients, number of ambulances and hospital selection rules, on the casualty transportation system’s performance in large-scale disasters. Besides, a data-driven DST for casualty transportation is proposed to enhance the casualty survival and ambulance transportation times during the disaster response stage.

In this study, the authors applied simulation and statistical analysis to evaluate the effects of usage of data-driven DST, classification of patients (triage), prioritization of the patients based on vital scores, number of ambulances and hospital selection rules, on the patient survival and transportation time of the casualty transportation system. An experimental design was made, and 16 scenarios were formulated. Simulation models were developed for all scenarios. The number of unrecoverable casualties and time-spent by the casualties until arriving at the hospital was observed. Then, a statistical analysis was applied to the simulation results, and significant factors were determined.

Utilization of the proposed DST was found to improve the casualty transportation and coordination performance. All main effects of the design factors were found statistically significant for the number of unrecoverable casualties. Besides, for the Time spent Until Arrival of T1-Type Casualty at the Hospital, all of the main factors are significant except the number of ambulances. Respiratory rate, pulse rate, motor response score priority and hospital selection rule based on available hospital capacities must be considered to reduce the number of unrecoverable casualties and time spent until arrival of the casualties at the hospitals.

In this study, the factors that significantly affect the performance of the casualty transportation system were revealed, by simulation and statistical analysis, based on an expected earthquake case, in a metropolitan city. Besides, it was shown that using a data-driven DST that tracks victims and intends to support disaster coordination centers and medical staff performing casualty transportation significantly improves survival rate of the victims and time to deliver the casualties. This research considers the whole systems’ components, contributes to developing the response stage operations by filling gaps between using the data-driven DST and casualty transportation processes.