RESilient Emergency Preparedness for Natural Disaster Response through Operational Research(RESPOND-OR)

自然灾害对人类、社会和经济环境造成严重后果。虽然大规模自然灾害发生在世界各地，但统计证据表明，其负面影响在欠发达国家更为明显。2003至2013年间，发展中国家的自然灾害造成了约5500亿美元的损失，影响了20亿人。印度尼西亚和苏丹是受自然灾害经济和社会后果影响最大的国家之一。在印度尼西亚，一场自然灾害可能同时或按顺序引发另一场自然灾害。此外，印度尼西亚的救灾需要考虑到该国的群岛结构。在苏丹，普遍存在的洪灾可能会引发需要同时考虑的卫生紧急情况。此外，苏丹的救灾行动因国内冲突而具有高风险的特点，优化备灾和救灾干预措施提供了充分的潜力，可以减少灾害的负面影响，对受影响社区的可持续性具有重大的经济和社会效益。然而，现有的方法大多基于一般假设，这往往会过度简化灾害管理机构的决策需求。具体地说，现有的备灾和应对模型没有充分处理以下挑战：1.为同时和/或顺序发生的合并的大规模自然灾害，即引发海啸的地震或洪灾后的疾病暴发，建立灾害应对资源分配的模型。战略备灾和业务救灾决策的综合建模。3.在公民抗命和社会冲突的情况下模拟人道主义支助资源的路线和调度。4.在模拟备灾和应对决定时纳入公平标准。缺乏能够反映现实世界复杂性的模型，导致对稀缺的备灾和应对资源的分配和使用效率低下。因此，迫切需要解决因灾害管理机构现实世界决策环境的复杂性而产生的数学建模以及相关的计算和数据管理挑战。Response-OR项目将开发下一代模型，该模型将纳入所有相关利益攸关方的要求。所提出的模型的复杂性将需要开发新的超启发式算法，以在非常短的计算时间内提供高质量的解。数学模型、解决方案算法以及数据管理和可视化工具将为开发决策支持系统奠定基础，该系统将加强印度尼西亚和苏丹备灾和应对灾害组织的决策能力。该研究小组在数学建模、启发式开发、随机优化、数据管理和可视化以及备灾和应对管理等领域具有国际领先地位。该研究团队在利益相关者参与方面有着出色的记录。我们将与我们的利益攸关方合作伙伴密切合作，确保响应或响应的结果是科学合理的，并完全符合他们的需求。

Natural disasters have grave consequences for human, social and economic environment. Although large-scale natural disasters occur worldwide, statistical evidence suggests that their negative impacts are much more pronounced in less developed countries. Between 2003-2013, natural disasters in developing countries cost about $550 billion and affected 2 billion people. Indonesia and Sudan are among the countries enormously affected by the economic and societal consequences of natural disasters. In Indonesia, a natural disaster may trigger another natural disaster, either simultaneously or in a sequential order. In addition, the disaster response in Indonesia needs to take into consideration the archipelago structure of the country. In Sudan, the prevalent disaster, which is flooding may trigger a health emergency that requires simultaneous consideration. Furthermore, the disaster response operations in Sudan are characterized by high risk due to civil conflicts.The optimization of disaster preparedness and response interventions provides ample potential to decrease the magnitude of the negative impacts of the disasters with significant economic and societal benefits for the sustainability of the impacted communities. However, available approaches are mostly based on generic assumptions that tend to oversimplify the decision-making needs of disaster management agencies. Specifically, available disaster preparedness and response models do not adequately address the following challenges:1. Modelling of the allocation of disaster response resources for combined large-scale natural disasters that happen simultaneously and/or sequentially, i.e. earthquakes triggering tsunamis, or outbreak of diseases following floods.2. Integrated modelling of strategic disaster preparedness and operational disaster relief decisions. 3. Modelling the routing and scheduling of humanitarian support resources in the presence of civil disobedience and social conflict. 4. The incorporation of fairness criteria in modelling disaster preparedness and response decisions. The lack of models capturing the real world complexities leads to inefficient allocation and use of scarce disaster preparedness and response resources. Therefore, there is an urgent need to address the mathematical modelling and associated computational and data management challenges stemming from the complexity of the real world decision-making environment of disaster management agencies. The RESPOND-OR project will develop the next generation of models which will incorporate the requirements of all relevant stakeholders. The complexity of the proposed models will necessitate the development of new hyper heuristics that will provide good quality solutions in very short computational times. The mathematical models, the solution algorithms, and the data management and visualization tools will underpin the development of a Decision Support System (DSS) that will enhance the decision-making capabilities of disaster preparedness and response organizations in Indonesia and Sudan. The research team has an internationally leading profile in the areas of mathematical modelling, heuristic development, stochastic optimization, data management and visualization, and disaster preparedness and response management. The research team has an excellent record in stakeholder engagement. We will work very closely with our stakeholder partners to ensure that the outcome of RESPOND-OR will be scientifically sound and fully aligned with their needs.