Abstract: This paper focuses on the modeling of the resources location of medical rescue.At first，the traditional Lagrangean relaxation algorithm is improved by using subgradient optimization algorithm.The iterative Lagrangean multipliers and improved LocAlloc algorithm are used to realize the gap optimization between upper and lower bounds of Lagrangean relaxation algorithm and improve the convergence rate.In addition，the greedy algorithm locates the resources of medical rescue sequentially with an attempt to cover the most uncovered demand points and gets the optimal solution of maximal coverage.Eventually，mass medical rescue for Wenchuan earthquake is provided to demonstrate the validity and feasibility of this model and algorithm.

Key words: large-scale emergency, medical rescue, resources location

摘要： 以医疗急救资源的配置问题为建模核心，运用次梯度最优算法对传统的拉格朗日松弛算法进行了改进。经迭代的拉格朗日乘数和改进LocAlloc算法对其上下限值的间距进行优化，加快了收敛速度，而贪婪算法可以连续完成对未覆盖的需求点的搜寻，实现了有效的医疗急救资源最优覆盖解。通过汶川地震的大规模医疗急救案例验证了该模型及算法的有效性和可行性。

关键词: 大规模突发事件, 医疗援救, 资源配置