运筹与管理 ›› 2020, Vol. 29 ›› Issue (9): 62-69.DOI: 10.12005/orms.2020.0228

• 理论分析与方法探讨 • 上一篇    下一篇

基于两阶段混合整数规划模型的洪涝灾害应急管理研究

张庆1, 余淼2   

  1. 1. 南京航空航天大学 经济与管理学院, 江苏 南京 211106;
    2. 浙江大学 管理学院, 浙江 杭州 310058
  • 收稿日期:2018-06-24 出版日期:2020-09-25
  • 作者简介:张庆(1971-), 男, 贵州人, 副教授, 博士, 研究方向:物流与供应链管理, 项目管理;余淼(1997-), 男, 安徽人, 博士研究生, 研究方向:服务科学与运作管理。

Research on Flood Disaster Emergency Management Based on Two-stage Mixed Integer Programming Model

ZHANG Qing1, YU Miao2   

  1. 1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
    2. School of Management, Zhejiang University, Hangzhou 310058, China
  • Received:2018-06-24 Online:2020-09-25

摘要: 本文以洪涝自然灾害为现实背景, 考虑多种应急物资、灾情的不确定性和应急救灾的多目标性, 集成优化灾前准备和灾后响应两阶段, 建立了一定最大救援时间下的两阶段多目标混合整数规划模型。模型的目标一是使得不同灾害情景下灾后响应阶段总物资不足惩罚和延误损失的期望最小, 目标二是使得灾前准备阶段应急物资存储点建造成本、物资存储成本及灾后响应阶段物资分配成本之和最小。该模型保证了应急救灾的及时有效以及物资的公平分配。本文设计了一种多目标遗传算法用于模型求解, 结合具体算例, 得到了模型在最大救援时间为4到9区间内任意数值下的pareto最优解, 很好地适应了决策者不同的决策需求, 并根据pareto应急方案的数目, 灾后响应阶段成本期望和两阶段总成本等模型的三个关键产出随最大救援时间的变化趋势, 得出最优的最大救援时间为5.7。

关键词: 应急管理, 洪涝灾害, 混合整数规划模型, 多目标遗传算法, 最大救援时间

Abstract: On the background of flood disasters, this paper considers various emergency supplies, the uncertainty of disaster events and the multiple otjectives of emergency response, combining the emergency preparation with the emergency response, and establishes a two stages bi-objective mixed integer programming model under a certain maximum rescue time. The first objective of the model is to minimize the expectation of penalty due to supply shortage and the loss owing to distribution delay in the emergency response stage under different disaster scenarios. The second objective is to minimize the total cost of the two stages. The model ensures the timely and effective response to flood disaster and the equity of supply distribution.In this paper, a multi-objective genetic algorithm is developed to solve the model and the pareto-optimal decision schemes are obtained at any maximum rescue time within the range of four to nine, which well satisfies the different needs of the decision maker. Finally, the paper suggests that the optimal maximum rescue time can be set to 5.7 from the trend in our case.

Key words: emergency management, flood disaster, mixed integer programming, multi-objective genetic algorithm, maximum rescue time

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