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Advanced Approaches for Novel Emergency Response Systems in Stochastic Operations...
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Advanced Approaches for Novel Emergency Response Systems in Stochastic Operations Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 5343

Special Issue Editor

Dr. Sasan Barak

E-Mail Website
Guest Editor
Southampton Business School, University of Southampton, Southampton SO16 7PP, UK
Interests: machine learning; artificial intelligence; energy forecasting; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue endeavours to examine methods for the minimization and regulation of Emergency Response Systems from the perspective of Stochastic Operations Research. The published articles are expected to generate practical knowledge, underpinned by theoretical frameworks, which can be applied in the industrial, governmental, public, and healthcare sectors.

This Special Issue encompasses various areas in management science and operations research that are crucial to the effective management of emergency responses. These areas include, but are not restricted to:

  1. Emergency Medical Services (EMS): This sub-area deals with providing medical care to patients who require immediate attention during an emergency.
  2. Fire Services: This sub-area deals with preventing, containing, and extinguishing fires.
  3. Search and Rescue: This sub-area involves locating and rescuing people who are lost, missing, or trapped during an emergency.
  4. Disaster Response: This sub-area deals with the preparation, response, and recovery efforts in the aftermath of a natural or man-made disaster.
  5. Critical Infrastructure Protection: This sub-area involves protecting essential facilities and assets such as power plants, water treatment facilities, and transportation systems from threats and attacks.
  6. Public Health and Environmental Response: This sub-area deals with responding to public health emergencies such as pandemics and environmental crises such as oil spills or hazardous material incidents.

Dr. Sasan Barak
Guest Editor

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Keywords

  • disaster management
  • emergency preparedness
  • risk assessment
  • hazard mitigation
  • search and rescue
  • public health emergency
  • mass casualty incident
  • emergency operations centre
  • disaster recovery
  • business continuity planning

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Published Papers (3 papers)

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Research

18 pages, 3370 KiB  
Article
A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
by Ayman Noor, Ziad Algrafi, Basil Alharbi, Talal H. Noor, Abdullah Alsaeedi, Reyadh Alluhaibi and Majed Alwateer
Appl. Sci. 2024, 14(6), 2555; https://doi.org/10.3390/app14062555 - 19 Mar 2024
Cited by 2 | Viewed by 2593
Abstract
Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning [...] Read more.
Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively. Full article
(This article belongs to the Special Issue Advanced Approaches for Novel Emergency Response Systems in Stochastic Operations Research)
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16 pages, 3450 KiB  
Article
Multi-Level Site Selection of Mobile Emergency Logistics Considering Safety Stocks
by Ruochen Zhang, Jianxun Li and Yanying Shang
Appl. Sci. 2023, 13(20), 11245; https://doi.org/10.3390/app132011245 - 13 Oct 2023
Cited by 2 | Viewed by 1147
Abstract
With the increasing frequency of emergencies in recent years, the emergency response capacity of the emergency management system needs to be improved. Based on safety stock strategy, this paper proposes a multilevel siting model on the topic of mobile emergency response. We modeled [...] Read more.
With the increasing frequency of emergencies in recent years, the emergency response capacity of the emergency management system needs to be improved. Based on safety stock strategy, this paper proposes a multilevel siting model on the topic of mobile emergency response. We modeled the emergency response needs during emergencies by incorporating the population distribution of each region. The uncertainty of emergencies is modeled by aggregating the frequency of crises in each region over the past 20 years. The site selection model minimizes contingency logistics costs that include transshipment, deployment, inventory, and safety stock costs. In this paper, the IA (Immune Algorithm) is optimized to solve the constructed emergency site selection model. The experiments on the model were carried out with data from the area of Chongqing, Sichuan Province. The number of logistics centers and distribution storage warehouses was tested. The influence of safety stock strategy on the total cost of emergency logistics was analyzed. The research results found that the cost of safety stock is negatively related to the cost of transshipment. In addition, the total cost of emergency logistics has a lower bound. Adding distribution and storage warehouses does not further reduce the total emergency logistics cost. Full article
(This article belongs to the Special Issue Advanced Approaches for Novel Emergency Response Systems in Stochastic Operations Research)
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17 pages, 3369 KiB  
Article
Scheduling Optimization of Mobile Emergency Vehicles Considering Dual Uncertainties
by Jianxun Li, Haoxin Fu, Kin Keung Lai, Ruochen Zhang and Muhammad Babar Iqbal
Appl. Sci. 2023, 13(19), 10670; https://doi.org/10.3390/app131910670 - 25 Sep 2023
Cited by 1 | Viewed by 1204
Abstract
Compared with the traditional operation mode of emergency vehicles, the mobile emergency vehicle is regarded as a new type of emergency facility carrier with the features of variable locations, flexible mobility, and intelligent decision-making. It can provide an effective solution to reasonably respond [...] Read more.
Compared with the traditional operation mode of emergency vehicles, the mobile emergency vehicle is regarded as a new type of emergency facility carrier with the features of variable locations, flexible mobility, and intelligent decision-making. It can provide an effective solution to reasonably respond to the uncertain risks of sudden disasters. Focusing on meeting the maximum demand for materials and services in disaster areas, this paper proposes a scheduling model of mobile emergency vehicles with dual uncertainty of path and demand. The model, solved by an integer-coding hybrid genetic algorithm, aims to obtain minimum mobile emergency scheduling cost and time by transforming the multi-objective problem into a single-objective problem. The “5.12” Wenchuan earthquake is used as an example to validate the model and solving method. The results show that the model can reduce the impact of uncertain risks and improve the scientific logic of emergency strategies and deployments based on the actual crisis scenario. It benefits from introducing mobile emergency vehicles and optimizing their scheduling process. Full article
(This article belongs to the Special Issue Advanced Approaches for Novel Emergency Response Systems in Stochastic Operations Research)
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