Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study

Caglayan, Nadide; Satoğlu, Şule Itır

doi:10.3390/math9040316

Cited by 15 publications

(6 citation statements)

References 39 publications

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“…Their proposal did not consider large-scale emergency scenarios, but rather everyday medical emergencies in which ambulances are individually assigned to care locations. Likewise, Caglayan and Satoglu [26] proposed a multi-objective model with three objectives: minimize the number of victims unable to be transported to hospitals after the disaster, reduce the number of additional ambulances needed in the response phase, and reduce the total transport time. Caglayan and Satoglu considered the assignment of triage points to emergency stations prior to the event and decided the number of ambulances that may be needed in advance.…”

Section: Related Workmentioning

confidence: 99%

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medina-Perez,

Guzmán,

Saldana-Perez

et al. 2024

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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%

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medina-Perez,

Guzmán,

Saldana-Perez

et al. 2024

Information

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“…A computational study was made for a hypothetical case of a gas leak in Bhopal, India. Caglayan and Satoglu (2021) developed a multi-objective two-stage stochastic programming model to minimize the number of untransported casualties, additional ambulance requirements and total transportation time. The proposed model was applied to a district in Istanbul, Turkey, in the context of a major earthquake.…”

Section: Casualty Allocation/transportationmentioning

confidence: 99%

Integrated optimization of facility location, casualty allocation and medical staff planning for post-disaster emergency response

Oksuz,

Satoglu

2023

JHLSCM

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Purpose Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively. Design/methodology/approach This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics. Findings Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h. Originality/value This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

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“…Ozbay et al (2019) propose a three-stage stochastic mixed-integer programming model to study the shelter sites location-allocation problem under earthquakes. Caglayan and Satoglu (2021) build a multi-objective two-stage stochastic programming model for casualty transportation, aimed at minimising the number of casualties that could not be transported to the hospitals after the disaster, the number of additional ambulances required in the response stage and the total transportation time. Chou et al (2022) propose a patient transportation and assignment model considering routing of ambulances and operational conditions of hospitals for an efficient mass casualty incident response.…”

Section: Introductionmentioning

confidence: 99%

Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief

Zhou

Gong²,

Hu³

et al. 2022

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PurposeThe purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.Design/methodology/approachDifferent from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.FindingsA case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.Research limitations/implicationsThe casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.Practical implicationsConsidering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.Originality/value(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.

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Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study

Cited by 15 publications

References 39 publications

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Integrated optimization of facility location, casualty allocation and medical staff planning for post-disaster emergency response

Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief

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