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New publication on problem of developing a rescue plan in disaster response

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Abstract

Disasters pose a serious threat to people’ lives and urban environment, affecting the sustainable development of society. Then it's crucial to quickly develop an efficient rescue plan for the disaster area. However, disaster rescue is rather difficult due to the requirement to develop the optimal rescue plan as quickly as possible according to the information of trapped people and rescue teams, and the amount of information will continue to increase as the rescue proceeds. At present, most of the rescue plans are manually made based on previous rescue experience. But obviously these plans might be the not optimal one. Considering the real-time location data of trapped people, this paper develops a Mixed Integer Non-linear Programming (MINLP) model to find the highest efficient rescue plan To solve the model accurately and efficiently, a bi-level decomposition (BLD) algorithm is presented to iteratively solve a discretized Mixed Integer Linear Programming (MILP) model and its nonconvex Non-linear Programming (NLP) model until a converged solution is obtained. In addition, since more trapped people could be found over time, the built rescue units should also be considered when making a rescue plan for a new stage. To further improve the solving efficiency, an accelerated bi-level decomposition (ABLD) algorithm is also proposed. Finally, a real-world disaster rescue is given to validate the superiority of the proposed ABLD algorithm relative to particle swarm optimization (PSO) algorithm and BLD algorithm.

The BDEM researchers at the European Climate Change Adaptation conference

Dr. Minsung Hong presenting the BDEM research

Dr. Minsung Hong presenting the BDEM research

Dr. Minsung Hong and Professor Rajendra Akerkar (Western Norway Research Institute) participated in the 4th European Climate Change Adaptation conference at Lisbon from 28-31 May 2019. The biennial European Climate Change Adaptation conference is convened by international and European projects on behalf of the European Commission.

Dr. Hong presented ongoing research, in the Big Data Research Group, entitled "Improving Resilience to Extreme Events" at the conference. The research addresses the gap in knowledge that impedes a scalable and versatile emergency data integration and proposes the state-of-the-art technology in the field to a new level through enhanced techniques that bring together machine learning, big data analytics and semantic technologies. This work elaborates our experience in enhancing situational awareness in order to support decision making during extreme climate events by capitalizing on the integration and aggregated analysis of mobility, meteorological, historical, forecasting data, as well as of a multitude of pertinent multimedia data inputs.

Interaction and collaboration with the disaster risk reduction (DRR) community is a critical element in improving climate change adaptation (CCA), as the communities share similar goals and activities. This is important in relation to the goals and targets of three major international agreements: Sendai Framework for DRR and the UN Sustainable Development Goals.