Optimization of urban emergency support material distribution under major public health emergencies based on improved sparrow search algorithm.

The outbreak of major public health emergencies such as the coronavirus epidemic has put forward new requirements for urban emergency management procedures. Accuracy and effective distribution model of emergency support materials, as an effective tool to inhibit the deterioration of the public health sector, have gradually become a research hotspot. The distribution of urban emergency support devices, under the secondary supply chain structure of "material transfer center-demand point," which may involve confusing demands, is studied to determine the actual situation of fuzzy requests under the impact of an epidemic outbreak. An optimization model of urban emergency support material distribution, based on Credibility theory, is first constructed. Then an improved sparrow search algorithm, ISSA, was designed by introducing Sobol sequence, Cauchy variation and bird swarm algorithm into the structure of the classical SSA. In addition, numerical validation and standard test set validation were carried out and the experimental results showed that the introduced improved strategy effectively improved the global search capability of the algorithm. Furthermore, simulation experiments are conducted, based on Shanghai, and the comparison with existing cutting-edge algorithms shows that the designed algorithm has stronger superiority and robustness. And the simulation results show that the designed algorithm can reduce vehicle cost by 4.83%, time cost by 13.80%, etc. compared to other algorithms. Finally, the impact of preference value on the distribution of emergency support materials is analyzed to help decision-makers to develop reasonable and effective distribution strategies according to the impact of major public health emergencies. The results of the study provide a practical reference for the solution of urban emergency support materials distribution problems.

Research on Anti-epidemic Material Delivery Robot

After the occurrence of the COVID-19, preventing cross infection has become a top priority. Therefore, it is proposed to use robots to replace people to distribute anti epidemic materials, so as to reduce human contact. By planning the trajectory of the robot in advance, and using mechanical arms and claws to achieve accurate grasp and delivery of anti epidemic materials, it can carry out material distribution in the isolated inpatient department, and can independently locate and deliver products, goods, etc. in a complex environment. It has strong cargo carrying capacity, and has the dual functions of traditional delivery robots and indoor delivery services. Its use can greatly reduce the infection rate in the epidemic and deliver materials in time. © 2022 IEEE.

Blockchain Application in Emergency Materials Distribution Under Disaster: An Architectural Design and Investigation

In recent years, natural disasters have been raging around the world, among which COVID-19, a public health pandemic, is still spreading all over the world. Emergency material distribution (EMD) in rescue work is facing serious challenges in the term of coordination, efficiency, traceability, identification, credibility, and security, privacy, and transparency. To address the issues, this paper develops a framework based on blockchain technology so that the operation information of EMD can be safely and promptly transmitted to the blockchain system. The scheme could achieve to query, track and record the operation status of emergency material collection, vehicle scheduling, inventory management, relief material allocation and others in real time. By using this framework, a case study on medical rescue materials distribution for COVID-19 is provided to illustrate how blockchain technology and Internet of Things (IoT) technology can be effective. The research provides insights on using the disruptive blockchain technology to effectively coordinate the deployment of emergency supplies, vehicles and personnel, and improve the transparency and credibility of rescue work to minimize the losses caused by disasters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Deep Reinforcement Learning-based Vaccine Distribution Strategies

The rapid development of artificial intelligence techniques is significantly promoting the resolution of various important decision-making issues such as material distribution, generation line optimization scheduling, and path planning. Currently, SARS-CoV-2 is raging over the world, and it is valuable to propose a vaccine distribution strategy to utilize limited vaccine resources rationally. In this paper, we aim to propose an optimal vaccine distribution strategy based on deep reinforcement learning(DRL) approaches. An End-to-End vaccine distribution model is proposed by combining the Deep Reinforcement Learning model and LinUCB algorithm to get an optimistic strategy of allocation. Experiment results demonstrated that vaccine distribution strategies based on this model show a strong capacity to control the epidemic and ensure stable government revenue compared with baseline strategies. © 2021 IEEE.