【摘要】 质量安全、自然灾害、公共卫生等突发事件社区应急疏散中的灾民数量具有不确定性,但目前的研究很少关注多种运输方式协作的应急疏散中灾民数量的不确定性。针对灾民数量的不确定性,以疏散灾民数量最大化,以及疏散成本最小化作为优化目标,,该文构建了多种运输方式协作的社区应急疏散模糊机会约束规划模型。在模型求解时,论文使用自适应遗传算子对多目标遗传算法进行改进,以提高算法优化效率。最后,论文使用算例对提出的模型进行验证,证明了该模型和方法可以有效完成需求不确定条件下的社区应急疏散协作调度优化工作。更多还原

【Abstract】 For recent years, incidents have hit communities frequently. How to complete the task of resident evacuation is an important problem in public administration. Besides the requirement of varieties of transport tools for collaborative transportation, community evacuation is often characterized by the uncertainty of resident quantity. However, the current studies neither pay much attention to collaboration mode of evacuation with varieties of transportation tools nor consider the uncertainty of resident quantity. Considering the uncertainty caused by resident quantity, this paper constructs fuzzy chance constrained programming model for community evacuation through the coordination of varieties of transportation tools, aiming to accomplish the targets of evacuated victims maximized and evacuation cost minimized. Importantly, this paper designs adaptive genetic operators to improve multi-objective genetic algorithms to enhance the optimization efficiency. Lastly, this paper takes an example to test the proposed model, which proves the proposed model and method have high efficiency in the collaborative scheduling optimization for community evacuation under the condition of uncertainty demand.更多还原