A multi-period ambulance location and allocation problem in the disaster

Wang, Jian; Wu, Yin; Yu, Ming L.

doi:10.1007/s10878-020-00610-3

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…On the other hand, Wang et al [27] proposed a model for ambulance locationallocation in disaster management, addressing the identification of both temporary bases and medical centers with funding restrictions. It is a combination of SA, GA, and PSO.…”

Section: Related Workmentioning

confidence: 99%

“…The adaptive VNS metaheuristic was used to solve the problem. However, both [27,28] recommended for future work to extend model experiments with different travel times from temporary stations to demand points, considering different times of day, since in a disaster situation the closest temporary station will not always be the first to arrive. To address this issue, the model proposed in the present paper consulted the traffic data corresponding to the time of day of each scenario analyzed.…”

Section: Related Workmentioning

confidence: 99%

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Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medina-Perez,

Guzmán,

Saldana-Perez

et al. 2024

Information

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Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medina-Perez,

Guzmán,

Saldana-Perez

et al. 2024

Information

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show abstract

“…In the context of ambulance placement and allocation, Wang et al [7] addressed the identification of temporary bases and temporary medical centers with budget constraints. They proposed hybrid metaheuristics to assign ambulances to temporary stations and distribute them to affected areas.…”

mentioning

confidence: 99%

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents, Case Study: Mexico City

Medina-Pérez,

Guzmán,

Saldana-Pérez

et al. 2024

Preprint

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Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival in these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors such as insufficient prevention education, limited disaster assets, and inadequate coordination between public and private emergency services may complicate these situations. In large-scale emergencies, multiple hotspots are usually observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as an optimization problem, focusing on the location and allocation of resources in mass casualty incidents. The proposed case study is Mexico City in a post-earthquake scenario, using voluntary geographic information, open government data, and historical data from the September 19, 2017 earthquake. Additionally, it is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

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Location and capacity allocation for emergency contact points in large-scale power outages

Truden,

Wankmüller,

Zehetner

et al. 2024

Cent Eur J Oper Res

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Nowadays, industry and individuals alike are highly dependent on a reliable power supply. A large-scale power outage, commonly known as a “blackout” is caused by natural disasters, cyber attacks, technical failure, or human errors, and can lead to a variety of severe consequences. The far-reaching dynamics of blackouts can even result in the collapse of critical public service infrastructure reliant on electricity (e.g., communication, water supply, medical services, public safety). Particularly, the loss of information and communication infrastructure essential to reporting medical emergencies, and the collapse of the drinking water supply are two critical stressors for the population to cope with. One attempt to tackle this situation is to install temporary emergency contact points (ECPs) into existing infrastructure. These can be approached by the population to communicate with medical personnel and to receive drinking water. Different types of professional personnel, which is a limited resource, are required to run such ECPs. Our study introduces this tactical decision problem. We formulate it as an integer linear program for the optimal spatial allocation of ECPs, such that multiple types of human resources that are required for operating such locations can be efficiently assigned. A comprehensive numerical study, based on data of the City of Vienna, demonstrates how to reduce the walking distance of inhabitants while increasing the efficiency of resource allocation. Matrix pruning based on an enforced limit of the walking distances together with a decomposition approach is utilized to solve the considered instances.

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A multi-period ambulance location and allocation problem in the disaster

Cited by 6 publications

References 33 publications

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents, Case Study: Mexico City

Location and capacity allocation for emergency contact points in large-scale power outages

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